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The data set has 8946 rows and 262 columns Cost(zillow) and Revenue(Airbnb) datasets are first cleaned individually and then joined in order to improve the overall data quality.

Process: I have catered to the main issues with bad quality of data which are:
Missing Values, Duplicated Rows
We can delete rows with having more than 50% missing values and also making sure to check the effect of removing a particular variable by checking its overall variance using statistical modeling in SAS.

We can also use principal component analysis in order to reduce overal variables which wont impact the overall variance by much. Filter imbalanced columns, cater to categorical variables, high missing value columns, columns not associated to the problem statement etc. Analyze any redudant columns and aggregate based on reasoning/assumptions. Check for correlation between variables in order to detect multicollinearity which could be a big problem during statistical modelling.

Produce clean cost and revenue data for joining and further explore the joined data to derive meaningful insights.

cost <- read.csv("C:/Users/Kartik/Airbnb/Zillow_dataset.csv")
#View(cost)
dim(cost)
## [1] 8946  262
head(cost)
revenue <- read.csv("C:/Users/Kartik/Airbnb/airbnbbbbbbb.csv")
#View(revenue)


kable(head(cost))  %>% kable_styling (bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")
RegionID RegionName City State Metro CountyName SizeRank X1996.04 X1996.05 X1996.06 X1996.07 X1996.08 X1996.09 X1996.10 X1996.11 X1996.12 X1997.01 X1997.02 X1997.03 X1997.04 X1997.05 X1997.06 X1997.07 X1997.08 X1997.09 X1997.10 X1997.11 X1997.12 X1998.01 X1998.02 X1998.03 X1998.04 X1998.05 X1998.06 X1998.07 X1998.08 X1998.09 X1998.10 X1998.11 X1998.12 X1999.01 X1999.02 X1999.03 X1999.04 X1999.05 X1999.06 X1999.07 X1999.08 X1999.09 X1999.10 X1999.11 X1999.12 X2000.01 X2000.02 X2000.03 X2000.04 X2000.05 X2000.06 X2000.07 X2000.08 X2000.09 X2000.10 X2000.11 X2000.12 X2001.01 X2001.02 X2001.03 X2001.04 X2001.05 X2001.06 X2001.07 X2001.08 X2001.09 X2001.10 X2001.11 X2001.12 X2002.01 X2002.02 X2002.03 X2002.04 X2002.05 X2002.06 X2002.07 X2002.08 X2002.09 X2002.10 X2002.11 X2002.12 X2003.01 X2003.02 X2003.03 X2003.04 X2003.05 X2003.06 X2003.07 X2003.08 X2003.09 X2003.10 X2003.11 X2003.12 X2004.01 X2004.02 X2004.03 X2004.04 X2004.05 X2004.06 X2004.07 X2004.08 X2004.09 X2004.10 X2004.11 X2004.12 X2005.01 X2005.02 X2005.03 X2005.04 X2005.05 X2005.06 X2005.07 X2005.08 X2005.09 X2005.10 X2005.11 X2005.12 X2006.01 X2006.02 X2006.03 X2006.04 X2006.05 X2006.06 X2006.07 X2006.08 X2006.09 X2006.10 X2006.11 X2006.12 X2007.01 X2007.02 X2007.03 X2007.04 X2007.05 X2007.06 X2007.07 X2007.08 X2007.09 X2007.10 X2007.11 X2007.12 X2008.01 X2008.02 X2008.03 X2008.04 X2008.05 X2008.06 X2008.07 X2008.08 X2008.09 X2008.10 X2008.11 X2008.12 X2009.01 X2009.02 X2009.03 X2009.04 X2009.05 X2009.06 X2009.07 X2009.08 X2009.09 X2009.10 X2009.11 X2009.12 X2010.01 X2010.02 X2010.03 X2010.04 X2010.05 X2010.06 X2010.07 X2010.08 X2010.09 X2010.10 X2010.11 X2010.12 X2011.01 X2011.02 X2011.03 X2011.04 X2011.05 X2011.06 X2011.07 X2011.08 X2011.09 X2011.10 X2011.11 X2011.12 X2012.01 X2012.02 X2012.03 X2012.04 X2012.05 X2012.06 X2012.07 X2012.08 X2012.09 X2012.10 X2012.11 X2012.12 X2013.01 X2013.02 X2013.03 X2013.04 X2013.05 X2013.06 X2013.07 X2013.08 X2013.09 X2013.10 X2013.11 X2013.12 X2014.01 X2014.02 X2014.03 X2014.04 X2014.05 X2014.06 X2014.07 X2014.08 X2014.09 X2014.10 X2014.11 X2014.12 X2015.01 X2015.02 X2015.03 X2015.04 X2015.05 X2015.06 X2015.07 X2015.08 X2015.09 X2015.10 X2015.11 X2015.12 X2016.01 X2016.02 X2016.03 X2016.04 X2016.05 X2016.06 X2016.07 X2016.08 X2016.09 X2016.10 X2016.11 X2016.12 X2017.01 X2017.02 X2017.03 X2017.04 X2017.05 X2017.06
61639 10025 New York NY New York New York 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 798600 798800 801500 804600 814900 828300 835700 849300 858100 854000 834800 821700 830300 853700 868300 875200 882200 892400 905000 924000 934400 932100 927500 923600 907900 890900 883400 896100 923900 952900 964600 972500 973800 973400 966500 966800 967100 974800 976800 976100 973700 974500 973200 966400 950400 933300 920900 909400 891400 873300 858800 850200 842800 834000 828800 821400 813900 813300 821500 831700 845100 854500 858900 859200 863500 876000 886100 890000 894200 901800 909500 913300 907400 900000 897700 896300 892300 890400 888600 891700 899500 904400 908200 914000 915100 912300 914000 921100 923300 917300 915000 922800 929100 937700 955700 974200 995500 1019500 1035100 1054900 1079900 1092600 1103500 1118800 1139300 1154600 1144100 1120300 1125500 1136000 1135100 1130000 1138200 1153700 1174800 1185400 1188400 1189700 1193700 1199900 1201400 1202600 1214200 1235200 1258000 1287700 1307200 1313900 1317100 1327400 1338800 1350400 1356600 1358500 1364000 1373300 1382600 1374400 1364100 1366300 1354800 1327500 1317300 1333700 1352100 1390000 1431000
84654 60657 Chicago IL Chicago Cook 2 167700 166400 166700 167200 166900 166900 168000 170100 171700 173000 174600 177600 180100 182300 184400 186300 187600 189400 190300 189700 189800 191900 194500 195500 196000 196900 198900 201400 204600 207900 211800 214600 216000 217500 220200 222800 226200 229600 232400 234400 236300 238300 241800 246100 249500 251300 253200 255700 259200 263100 266600 269500 272800 275500 278800 283400 288600 291300 292400 294600 297100 298200 299800 302000 304200 307900 311000 311400 311000 311700 312300 312000 311800 312600 313000 314400 317300 319700 320500 321000 321600 323800 326100 329000 332200 334700 336000 337300 337500 337100 334900 333100 332800 333400 335100 337800 339400 340700 343200 345000 345200 344600 344500 346200 349800 353400 355100 356300 358300 359500 359200 358500 359100 361400 364700 367500 369400 369800 369600 368700 368100 369000 370300 371700 374200 375700 376400 378200 379800 380100 380400 381200 382700 382700 380200 377800 376300 375600 376000 376200 375800 376300 377200 376800 373700 370100 368700 368600 366600 362200 358600 355300 352300 350900 350100 347900 345400 343400 342800 342600 341100 339900 338900 338200 335200 329800 325500 323600 323400 325000 325800 323200 320100 318600 317400 315700 315000 315300 315600 313900 309800 305700 301800 299500 299900 301100 300300 298900 298500 298500 297000 296800 298700 299600 300700 303900 306800 307500 308500 310000 310800 311200 313000 315800 319000 323400 327500 330000 331800 334500 336000 335700 335400 336300 338800 342400 344400 344000 343900 345100 346100 346900 348000 349700 351200 351700 350700 350400 352000 354300 355900 356500 355200 353800 353700 354600 356200 357800 358200 358500 360300 362400 363700 365200 367100 368600 370200 372300 375300 378700 381400 381800 382100 383300 385100
61637 10023 New York NY New York New York 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1526800 1424500 1346600 1331300 1322500 1289300 1265400 1249700 1241100 1232700 1225500 1228200 1252600 1266100 1288700 1308100 1333000 1356400 1362000 1353600 1364000 1373900 1389600 1401600 1404100 1415800 1432400 1455400 1474200 1462300 1438300 1435500 1427800 1411200 1407400 1419700 1457400 1500800 1524900 1537800 1558700 1586100 1602300 1621100 1639300 1657400 1657400 1656100 1649400 1643400 1632400 1618200 1588300 1543600 1500800 1464200 1426100 1387300 1362600 1351700 1344300 1331800 1334800 1314200 1271900 1252300 1262300 1279200 1309000 1335300 1353800 1366400 1372100 1381300 1385000 1388100 1399100 1399800 1389300 1384700 1380900 1367900 1365400 1375100 1380400 1377000 1375100 1379000 1395200 1414500 1419000 1403100 1383200 1376700 1378200 1378700 1375900 1366700 1365500 1382200 1404700 1428000 1445700 1452900 1460100 1484400 1508400 1522800 1538300 1568600 1597400 1622900 1654300 1684600 1713000 1728800 1736100 1745900 1753800 1736600 1730400 1734500 1728700 1720800 1717700 1700100 1680400 1676400 1685600 1708100 1730400 1751800 1778300 1810400 1831600 1844400 1861600 1889600 1901500 1895300 1890200 1898400 1924500 1967300 1993500 1980700 1960900 1951300 1937800 1929800 1955000 2022400 2095000 2142300
84616 60614 Chicago IL Chicago Cook 4 195800 193500 192600 192300 192600 193600 195500 197600 199400 201300 203600 206500 209200 211100 212600 214400 215600 216500 217900 220100 222200 223900 225400 227700 230100 231700 232700 233700 234700 235600 236800 238800 240800 242400 243800 246400 250200 254300 257600 261100 264800 267900 270700 272800 274400 276200 278600 280100 283100 287700 293600 298500 302700 305000 306800 309400 313100 314900 316200 318200 320600 322900 325500 328400 330700 332800 334400 335900 337400 339700 342300 343800 343400 342300 341800 341700 342400 344300 346900 348900 350200 351700 353500 355700 358000 361600 364000 365500 366400 367000 365200 363100 362000 362500 364800 368200 370800 371400 371200 371800 374200 377800 380100 381700 384400 386600 385700 385400 388500 392200 394500 394900 394800 395500 400000 404300 407600 409500 410400 408700 406400 403800 402400 402300 403000 403100 403600 404500 406100 407700 408700 409600 409800 408700 408600 411000 412200 411400 410200 408400 406600 406500 406400 404600 402900 400600 397500 392600 387500 383700 381000 378900 377700 377400 376700 374000 369600 366200 364500 364100 364300 363600 361900 361900 356400 347100 342400 344000 345200 346900 346100 342600 340100 339900 338600 335500 333900 335000 336000 334200 330300 327000 326000 326100 326700 326300 324400 322700 323200 322800 320700 319500 320100 320500 321800 323600 324300 324100 324700 326000 327600 329800 332600 336800 342300 348100 353600 358900 361900 363900 366200 368300 369800 371400 372400 373200 373800 374800 376200 376800 376300 374900 373800 373900 374700 375300 375000 374700 376300 378100 378000 377700 378300 380000 383100 385900 388100 389700 391800 393400 394700 394900 395700 396400 397500 398900 401200 403200 405700 408300 408800 408000 410100 412200 412200
93144 79936 El Paso TX El Paso El Paso 5 59100 60500 60900 60800 60300 60400 61200 61700 61000 60100 59300 59000 58700 58400 58000 57800 57900 57800 57800 58100 58400 58700 59200 59400 58700 58100 57900 57900 57700 57600 57500 57800 58000 58000 57900 57800 57800 58000 58500 58700 59000 59200 59300 59500 59900 60300 60400 60300 60300 60000 59500 59400 59800 59900 59700 59500 59400 59500 59700 59700 59200 58700 58400 57800 57000 56700 56800 56600 56500 56500 56600 56700 56600 56700 57000 57300 57300 57300 57100 56900 56900 57000 56700 56700 57000 57400 57700 58000 58300 58600 58800 59100 59500 60200 61100 61700 62400 63100 63600 63800 64400 64900 65100 65200 65300 65600 66300 67100 67900 68800 69600 70600 71500 72100 72600 73400 74200 74500 75000 75700 76400 77100 77700 78100 78700 79600 80600 81500 82300 83200 83900 84400 84300 84400 85200 86100 86800 87400 87700 87800 88300 88800 88500 87900 87500 86600 85500 84600 84200 83900 83600 83400 83500 84100 84600 84600 84700 84600 84600 85100 85500 85800 86600 87600 86500 84200 83300 83800 83800 83600 83600 83400 82900 82400 82200 82100 82300 82800 82900 82600 82700 83100 83300 83100 82800 82500 82300 82200 82300 82200 81900 81700 82100 82600 82900 83000 83000 82900 82100 81200 80800 80700 81200 81800 81800 81400 81400 81500 81900 82000 81900 81900 82100 82100 81500 80800 80300 80100 80100 80700 81200 81700 81900 81700 81500 81700 81700 80900 81000 81500 81400 80500 80000 80100 80500 80800 81400 82300 82600 82600 82500 82500 82600 82700 82600 82400 82300 82400 82300 82500 83200 83900 84100 83900 83700
84640 60640 Chicago IL Chicago Cook 6 123300 122600 122000 121500 120900 120600 120900 121300 121600 122100 122900 124200 125300 126100 126700 127900 129300 130400 131300 131700 132300 133500 134500 134800 135200 135500 136300 137700 139600 141600 143400 144500 145600 147400 149200 149900 150500 151700 152800 153900 156400 159400 161800 163800 165700 167100 168400 169600 171000 172400 174800 177900 180400 182300 184600 187700 190700 193100 196100 200000 202900 205500 207600 208600 209200 210000 210200 211300 213000 214100 215200 217600 220200 222400 224600 227000 228600 230100 232400 234300 235300 236200 237000 237900 239200 241300 243600 244200 243800 244500 245500 245700 246400 247500 248000 249000 251100 252900 253700 256100 259400 261300 262200 263800 265300 267600 270500 272800 273700 273600 273200 273300 273500 273800 274300 274400 274400 275300 276600 278200 280600 283100 284400 284200 283000 282000 282400 283700 285200 286700 286700 284900 283300 282700 282100 280800 280300 280200 280100 279900 281200 282300 282100 279300 275800 273100 272600 271800 270300 268600 266900 264800 262900 260700 258500 256600 255000 252900 250600 248900 248900 250100 250300 250400 248000 243800 240200 239700 239000 238200 236800 235400 233300 231300 230300 228200 225200 223100 222300 220700 218300 215900 213200 210900 209600 208200 205900 204000 203200 202500 200800 199400 199900 201900 204500 207000 208100 207100 205300 204700 204700 205800 208600 211800 213500 213800 215100 218400 221500 223900 226100 227900 229100 230200 230600 230400 230000 229000 227600 226100 225700 226200 226500 226500 227100 227800 229400 231800 234100 235400 235100 233900 233700 235300 237200 238500 239300 239600 239500 240200 242700 244900 247700 249500 248800 247000 247300 248700 250800 252800 253800 253800 253400 254100 255100
#cost
#head(cost)

Missing Values Median Price for early years (1996-2014) has plenty of Null values as shown in the table below. This is not consistent across all Regionnames. In order to cater this we have to make sure to handle this null values in a more effective manner.

This data showcases the number of missing vlaues for each of the variables. Seeing this table helps us in understanding quality of each variables and implement methods for variable selection and feature engineering.

numMissingVal <-sapply(cost, function(x) sum(length(which(is.na(x)))))  
#numMissingVal

colnames(cost)
##   [1] "RegionID"   "RegionName" "City"       "State"      "Metro"     
##   [6] "CountyName" "SizeRank"   "X1996.04"   "X1996.05"   "X1996.06"  
##  [11] "X1996.07"   "X1996.08"   "X1996.09"   "X1996.10"   "X1996.11"  
##  [16] "X1996.12"   "X1997.01"   "X1997.02"   "X1997.03"   "X1997.04"  
##  [21] "X1997.05"   "X1997.06"   "X1997.07"   "X1997.08"   "X1997.09"  
##  [26] "X1997.10"   "X1997.11"   "X1997.12"   "X1998.01"   "X1998.02"  
##  [31] "X1998.03"   "X1998.04"   "X1998.05"   "X1998.06"   "X1998.07"  
##  [36] "X1998.08"   "X1998.09"   "X1998.10"   "X1998.11"   "X1998.12"  
##  [41] "X1999.01"   "X1999.02"   "X1999.03"   "X1999.04"   "X1999.05"  
##  [46] "X1999.06"   "X1999.07"   "X1999.08"   "X1999.09"   "X1999.10"  
##  [51] "X1999.11"   "X1999.12"   "X2000.01"   "X2000.02"   "X2000.03"  
##  [56] "X2000.04"   "X2000.05"   "X2000.06"   "X2000.07"   "X2000.08"  
##  [61] "X2000.09"   "X2000.10"   "X2000.11"   "X2000.12"   "X2001.01"  
##  [66] "X2001.02"   "X2001.03"   "X2001.04"   "X2001.05"   "X2001.06"  
##  [71] "X2001.07"   "X2001.08"   "X2001.09"   "X2001.10"   "X2001.11"  
##  [76] "X2001.12"   "X2002.01"   "X2002.02"   "X2002.03"   "X2002.04"  
##  [81] "X2002.05"   "X2002.06"   "X2002.07"   "X2002.08"   "X2002.09"  
##  [86] "X2002.10"   "X2002.11"   "X2002.12"   "X2003.01"   "X2003.02"  
##  [91] "X2003.03"   "X2003.04"   "X2003.05"   "X2003.06"   "X2003.07"  
##  [96] "X2003.08"   "X2003.09"   "X2003.10"   "X2003.11"   "X2003.12"  
## [101] "X2004.01"   "X2004.02"   "X2004.03"   "X2004.04"   "X2004.05"  
## [106] "X2004.06"   "X2004.07"   "X2004.08"   "X2004.09"   "X2004.10"  
## [111] "X2004.11"   "X2004.12"   "X2005.01"   "X2005.02"   "X2005.03"  
## [116] "X2005.04"   "X2005.05"   "X2005.06"   "X2005.07"   "X2005.08"  
## [121] "X2005.09"   "X2005.10"   "X2005.11"   "X2005.12"   "X2006.01"  
## [126] "X2006.02"   "X2006.03"   "X2006.04"   "X2006.05"   "X2006.06"  
## [131] "X2006.07"   "X2006.08"   "X2006.09"   "X2006.10"   "X2006.11"  
## [136] "X2006.12"   "X2007.01"   "X2007.02"   "X2007.03"   "X2007.04"  
## [141] "X2007.05"   "X2007.06"   "X2007.07"   "X2007.08"   "X2007.09"  
## [146] "X2007.10"   "X2007.11"   "X2007.12"   "X2008.01"   "X2008.02"  
## [151] "X2008.03"   "X2008.04"   "X2008.05"   "X2008.06"   "X2008.07"  
## [156] "X2008.08"   "X2008.09"   "X2008.10"   "X2008.11"   "X2008.12"  
## [161] "X2009.01"   "X2009.02"   "X2009.03"   "X2009.04"   "X2009.05"  
## [166] "X2009.06"   "X2009.07"   "X2009.08"   "X2009.09"   "X2009.10"  
## [171] "X2009.11"   "X2009.12"   "X2010.01"   "X2010.02"   "X2010.03"  
## [176] "X2010.04"   "X2010.05"   "X2010.06"   "X2010.07"   "X2010.08"  
## [181] "X2010.09"   "X2010.10"   "X2010.11"   "X2010.12"   "X2011.01"  
## [186] "X2011.02"   "X2011.03"   "X2011.04"   "X2011.05"   "X2011.06"  
## [191] "X2011.07"   "X2011.08"   "X2011.09"   "X2011.10"   "X2011.11"  
## [196] "X2011.12"   "X2012.01"   "X2012.02"   "X2012.03"   "X2012.04"  
## [201] "X2012.05"   "X2012.06"   "X2012.07"   "X2012.08"   "X2012.09"  
## [206] "X2012.10"   "X2012.11"   "X2012.12"   "X2013.01"   "X2013.02"  
## [211] "X2013.03"   "X2013.04"   "X2013.05"   "X2013.06"   "X2013.07"  
## [216] "X2013.08"   "X2013.09"   "X2013.10"   "X2013.11"   "X2013.12"  
## [221] "X2014.01"   "X2014.02"   "X2014.03"   "X2014.04"   "X2014.05"  
## [226] "X2014.06"   "X2014.07"   "X2014.08"   "X2014.09"   "X2014.10"  
## [231] "X2014.11"   "X2014.12"   "X2015.01"   "X2015.02"   "X2015.03"  
## [236] "X2015.04"   "X2015.05"   "X2015.06"   "X2015.07"   "X2015.08"  
## [241] "X2015.09"   "X2015.10"   "X2015.11"   "X2015.12"   "X2016.01"  
## [246] "X2016.02"   "X2016.03"   "X2016.04"   "X2016.05"   "X2016.06"  
## [251] "X2016.07"   "X2016.08"   "X2016.09"   "X2016.10"   "X2016.11"  
## [256] "X2016.12"   "X2017.01"   "X2017.02"   "X2017.03"   "X2017.04"  
## [261] "X2017.05"   "X2017.06"
colnames(revenue)
##   [1] "id"                                          
##   [2] "listing_url"                                 
##   [3] "scrape_id"                                   
##   [4] "last_scraped"                                
##   [5] "name"                                        
##   [6] "summary"                                     
##   [7] "space"                                       
##   [8] "description"                                 
##   [9] "experiences_offered"                         
##  [10] "neighborhood_overview"                       
##  [11] "notes"                                       
##  [12] "transit"                                     
##  [13] "access"                                      
##  [14] "interaction"                                 
##  [15] "house_rules"                                 
##  [16] "thumbnail_url"                               
##  [17] "medium_url"                                  
##  [18] "picture_url"                                 
##  [19] "xl_picture_url"                              
##  [20] "host_id"                                     
##  [21] "host_url"                                    
##  [22] "host_name"                                   
##  [23] "host_since"                                  
##  [24] "host_location"                               
##  [25] "host_about"                                  
##  [26] "host_response_time"                          
##  [27] "host_response_rate"                          
##  [28] "host_acceptance_rate"                        
##  [29] "host_is_superhost"                           
##  [30] "host_thumbnail_url"                          
##  [31] "host_picture_url"                            
##  [32] "host_neighbourhood"                          
##  [33] "host_listings_count"                         
##  [34] "host_total_listings_count"                   
##  [35] "host_verifications"                          
##  [36] "host_has_profile_pic"                        
##  [37] "host_identity_verified"                      
##  [38] "street"                                      
##  [39] "neighbourhood"                               
##  [40] "neighbourhood_cleansed"                      
##  [41] "neighbourhood_group_cleansed"                
##  [42] "city"                                        
##  [43] "state"                                       
##  [44] "zipcode"                                     
##  [45] "market"                                      
##  [46] "smart_location"                              
##  [47] "country_code"                                
##  [48] "country"                                     
##  [49] "latitude"                                    
##  [50] "longitude"                                   
##  [51] "is_location_exact"                           
##  [52] "property_type"                               
##  [53] "room_type"                                   
##  [54] "accommodates"                                
##  [55] "bathrooms"                                   
##  [56] "bedrooms"                                    
##  [57] "beds"                                        
##  [58] "bed_type"                                    
##  [59] "amenities"                                   
##  [60] "square_feet"                                 
##  [61] "price"                                       
##  [62] "weekly_price"                                
##  [63] "monthly_price"                               
##  [64] "security_deposit"                            
##  [65] "cleaning_fee"                                
##  [66] "guests_included"                             
##  [67] "extra_people"                                
##  [68] "minimum_nights"                              
##  [69] "maximum_nights"                              
##  [70] "minimum_minimum_nights"                      
##  [71] "maximum_minimum_nights"                      
##  [72] "minimum_maximum_nights"                      
##  [73] "maximum_maximum_nights"                      
##  [74] "minimum_nights_avg_ntm"                      
##  [75] "maximum_nights_avg_ntm"                      
##  [76] "calendar_updated"                            
##  [77] "has_availability"                            
##  [78] "availability_30"                             
##  [79] "availability_60"                             
##  [80] "availability_90"                             
##  [81] "availability_365"                            
##  [82] "calendar_last_scraped"                       
##  [83] "number_of_reviews"                           
##  [84] "number_of_reviews_ltm"                       
##  [85] "first_review"                                
##  [86] "last_review"                                 
##  [87] "review_scores_rating"                        
##  [88] "review_scores_accuracy"                      
##  [89] "review_scores_cleanliness"                   
##  [90] "review_scores_checkin"                       
##  [91] "review_scores_communication"                 
##  [92] "review_scores_location"                      
##  [93] "review_scores_value"                         
##  [94] "requires_license"                            
##  [95] "license"                                     
##  [96] "jurisdiction_names"                          
##  [97] "instant_bookable"                            
##  [98] "is_business_travel_ready"                    
##  [99] "cancellation_policy"                         
## [100] "require_guest_profile_picture"               
## [101] "require_guest_phone_verification"            
## [102] "calculated_host_listings_count"              
## [103] "calculated_host_listings_count_entire_homes" 
## [104] "calculated_host_listings_count_private_rooms"
## [105] "calculated_host_listings_count_shared_rooms" 
## [106] "reviews_per_month"
kable(as.data.frame(numMissingVal)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")
numMissingVal
RegionID 0
RegionName 0
City 0
State 0
Metro 0
CountyName 0
SizeRank 0
X1996.04 2662
X1996.05 2582
X1996.06 2582
X1996.07 2577
X1996.08 2576
X1996.09 2576
X1996.10 2576
X1996.11 2566
X1996.12 2566
X1997.01 2542
X1997.02 2113
X1997.03 2093
X1997.04 2093
X1997.05 2093
X1997.06 2091
X1997.07 2091
X1997.08 1994
X1997.09 1991
X1997.10 1991
X1997.11 1988
X1997.12 1984
X1998.01 1967
X1998.02 1822
X1998.03 1821
X1998.04 1865
X1998.05 1973
X1998.06 1961
X1998.07 1776
X1998.08 1742
X1998.09 1742
X1998.10 1730
X1998.11 1709
X1998.12 1707
X1999.01 1706
X1999.02 1689
X1999.03 1688
X1999.04 1688
X1999.05 1685
X1999.06 1675
X1999.07 1675
X1999.08 1652
X1999.09 1652
X1999.10 1652
X1999.11 1652
X1999.12 1652
X2000.01 1652
X2000.02 1633
X2000.03 1633
X2000.04 1631
X2000.05 1631
X2000.06 1631
X2000.07 1626
X2000.08 1598
X2000.09 1598
X2000.10 1598
X2000.11 1598
X2000.12 1598
X2001.01 1679
X2001.02 1580
X2001.03 1553
X2001.04 1552
X2001.05 1552
X2001.06 1551
X2001.07 1551
X2001.08 1546
X2001.09 1546
X2001.10 1546
X2001.11 1545
X2001.12 1545
X2002.01 1545
X2002.02 1538
X2002.03 1538
X2002.04 1537
X2002.05 1537
X2002.06 1537
X2002.07 1537
X2002.08 1524
X2002.09 1522
X2002.10 1521
X2002.11 1521
X2002.12 1521
X2003.01 1520
X2003.02 1499
X2003.03 1498
X2003.04 1498
X2003.05 1498
X2003.06 1498
X2003.07 1498
X2003.08 1485
X2003.09 1484
X2003.10 1483
X2003.11 1482
X2003.12 1480
X2004.01 1479
X2004.02 1447
X2004.03 1428
X2004.04 1428
X2004.05 1428
X2004.06 1426
X2004.07 1426
X2004.08 1403
X2004.09 1398
X2004.10 1392
X2004.11 1389
X2004.12 1389
X2005.01 1388
X2005.02 1321
X2005.03 1305
X2005.04 1303
X2005.05 1303
X2005.06 1303
X2005.07 1303
X2005.08 1288
X2005.09 1288
X2005.10 1286
X2005.11 1285
X2005.12 1285
X2006.01 1284
X2006.02 1260
X2006.03 1260
X2006.04 1259
X2006.05 1259
X2006.06 1259
X2006.07 1259
X2006.08 1250
X2006.09 1249
X2006.10 1248
X2006.11 1248
X2006.12 1248
X2007.01 1247
X2007.02 1242
X2007.03 1240
X2007.04 1239
X2007.05 1239
X2007.06 1238
X2007.07 1238
X2007.08 1226
X2007.09 1226
X2007.10 1226
X2007.11 1226
X2007.12 1226
X2008.01 1226
X2008.02 1225
X2008.03 1225
X2008.04 1225
X2008.05 1225
X2008.06 1225
X2008.07 1225
X2008.08 1194
X2008.09 1194
X2008.10 1194
X2008.11 1194
X2008.12 1194
X2009.01 1193
X2009.02 1190
X2009.03 1184
X2009.04 1178
X2009.05 1178
X2009.06 1146
X2009.07 1146
X2009.08 1095
X2009.09 1095
X2009.10 1095
X2009.11 1094
X2009.12 1091
X2010.01 1091
X2010.02 1082
X2010.03 971
X2010.04 959
X2010.05 944
X2010.06 923
X2010.07 903
X2010.08 177
X2010.09 177
X2010.10 174
X2010.11 174
X2010.12 174
X2011.01 163
X2011.02 151
X2011.03 149
X2011.04 149
X2011.05 149
X2011.06 147
X2011.07 134
X2011.08 121
X2011.09 120
X2011.10 120
X2011.11 120
X2011.12 118
X2012.01 108
X2012.02 93
X2012.03 91
X2012.04 91
X2012.05 91
X2012.06 87
X2012.07 84
X2012.08 67
X2012.09 66
X2012.10 65
X2012.11 65
X2012.12 65
X2013.01 64
X2013.02 31
X2013.03 26
X2013.04 21
X2013.05 21
X2013.06 20
X2013.07 20
X2013.08 0
X2013.09 0
X2013.10 0
X2013.11 0
X2013.12 0
X2014.01 0
X2014.02 0
X2014.03 0
X2014.04 0
X2014.05 0
X2014.06 0
X2014.07 0
X2014.08 0
X2014.09 0
X2014.10 0
X2014.11 0
X2014.12 0
X2015.01 0
X2015.02 0
X2015.03 0
X2015.04 0
X2015.05 0
X2015.06 0
X2015.07 0
X2015.08 0
X2015.09 0
X2015.10 0
X2015.11 0
X2015.12 0
X2016.01 0
X2016.02 18
X2016.03 18
X2016.04 18
X2016.05 18
X2016.06 0
X2016.07 0
X2016.08 0
X2016.09 0
X2016.10 0
X2016.11 0
X2016.12 3
X2017.01 0
X2017.02 0
X2017.03 0
X2017.04 0
X2017.05 0
X2017.06 0
# to check non negative values

costCharCol <- colnames(cost %>% ungroup() %>% select_if(is.character))
#costCharCol
costZeroNeg <- sapply(cost[, !(names(cost) %in% costCharCol)], function(x)
 count(x <= 0, na.rm = TRUE))
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
kable(as.data.frame(costZeroNeg)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")
costZeroNeg
RegionID 0
RegionName 0
City 0
State 0
Metro 0
CountyName 0
SizeRank 0
X1996.04 0
X1996.05 0
X1996.06 0
X1996.07 0
X1996.08 0
X1996.09 0
X1996.10 0
X1996.11 0
X1996.12 0
X1997.01 0
X1997.02 0
X1997.03 0
X1997.04 0
X1997.05 0
X1997.06 0
X1997.07 0
X1997.08 0
X1997.09 0
X1997.10 0
X1997.11 0
X1997.12 0
X1998.01 0
X1998.02 0
X1998.03 0
X1998.04 0
X1998.05 0
X1998.06 0
X1998.07 0
X1998.08 0
X1998.09 0
X1998.10 0
X1998.11 0
X1998.12 0
X1999.01 0
X1999.02 0
X1999.03 0
X1999.04 0
X1999.05 0
X1999.06 0
X1999.07 0
X1999.08 0
X1999.09 0
X1999.10 0
X1999.11 0
X1999.12 0
X2000.01 0
X2000.02 0
X2000.03 0
X2000.04 0
X2000.05 0
X2000.06 0
X2000.07 0
X2000.08 0
X2000.09 0
X2000.10 0
X2000.11 0
X2000.12 0
X2001.01 0
X2001.02 0
X2001.03 0
X2001.04 0
X2001.05 0
X2001.06 0
X2001.07 0
X2001.08 0
X2001.09 0
X2001.10 0
X2001.11 0
X2001.12 0
X2002.01 0
X2002.02 0
X2002.03 0
X2002.04 0
X2002.05 0
X2002.06 0
X2002.07 0
X2002.08 0
X2002.09 0
X2002.10 0
X2002.11 0
X2002.12 0
X2003.01 0
X2003.02 0
X2003.03 0
X2003.04 0
X2003.05 0
X2003.06 0
X2003.07 0
X2003.08 0
X2003.09 0
X2003.10 0
X2003.11 0
X2003.12 0
X2004.01 0
X2004.02 0
X2004.03 0
X2004.04 0
X2004.05 0
X2004.06 0
X2004.07 0
X2004.08 0
X2004.09 0
X2004.10 0
X2004.11 0
X2004.12 0
X2005.01 0
X2005.02 0
X2005.03 0
X2005.04 0
X2005.05 0
X2005.06 0
X2005.07 0
X2005.08 0
X2005.09 0
X2005.10 0
X2005.11 0
X2005.12 0
X2006.01 0
X2006.02 0
X2006.03 0
X2006.04 0
X2006.05 0
X2006.06 0
X2006.07 0
X2006.08 0
X2006.09 0
X2006.10 0
X2006.11 0
X2006.12 0
X2007.01 0
X2007.02 0
X2007.03 0
X2007.04 0
X2007.05 0
X2007.06 0
X2007.07 0
X2007.08 0
X2007.09 0
X2007.10 0
X2007.11 0
X2007.12 0
X2008.01 0
X2008.02 0
X2008.03 0
X2008.04 0
X2008.05 0
X2008.06 0
X2008.07 0
X2008.08 0
X2008.09 0
X2008.10 0
X2008.11 0
X2008.12 0
X2009.01 0
X2009.02 0
X2009.03 0
X2009.04 0
X2009.05 0
X2009.06 0
X2009.07 0
X2009.08 0
X2009.09 0
X2009.10 0
X2009.11 0
X2009.12 0
X2010.01 0
X2010.02 0
X2010.03 0
X2010.04 0
X2010.05 0
X2010.06 0
X2010.07 0
X2010.08 0
X2010.09 0
X2010.10 0
X2010.11 0
X2010.12 0
X2011.01 0
X2011.02 0
X2011.03 0
X2011.04 0
X2011.05 0
X2011.06 0
X2011.07 0
X2011.08 0
X2011.09 0
X2011.10 0
X2011.11 0
X2011.12 0
X2012.01 0
X2012.02 0
X2012.03 0
X2012.04 0
X2012.05 0
X2012.06 0
X2012.07 0
X2012.08 0
X2012.09 0
X2012.10 0
X2012.11 0
X2012.12 0
X2013.01 0
X2013.02 0
X2013.03 0
X2013.04 0
X2013.05 0
X2013.06 0
X2013.07 0
X2013.08 0
X2013.09 0
X2013.10 0
X2013.11 0
X2013.12 0
X2014.01 0
X2014.02 0
X2014.03 0
X2014.04 0
X2014.05 0
X2014.06 0
X2014.07 0
X2014.08 0
X2014.09 0
X2014.10 0
X2014.11 0
X2014.12 0
X2015.01 0
X2015.02 0
X2015.03 0
X2015.04 0
X2015.05 0
X2015.06 0
X2015.07 0
X2015.08 0
X2015.09 0
X2015.10 0
X2015.11 0
X2015.12 0
X2016.01 0
X2016.02 0
X2016.03 0
X2016.04 0
X2016.05 0
X2016.06 0
X2016.07 0
X2016.08 0
X2016.09 0
X2016.10 0
X2016.11 0
X2016.12 0
X2017.01 0
X2017.02 0
X2017.03 0
X2017.04 0
X2017.05 0
X2017.06 0
# In order to find duplicate rows in the dataset
cost[which(duplicated(cost) ==T),] 
#Selecting the city in the dataset
cost <- cost %>% filter(City == "New York")
cost$City
##  [1] New York New York New York New York New York New York New York
##  [8] New York New York New York New York New York New York New York
## [15] New York New York New York New York New York New York New York
## [22] New York New York New York New York
## 4684 Levels: Aberdeen Abilene Abingdon Abington Acton Acushnet ... Zumbro Falls
#Creating a list of the years
yearList <- as.character(as.list(2005:2017))
yearList
##  [1] "2005" "2006" "2007" "2008" "2009" "2010" "2011" "2012" "2013" "2014"
## [11] "2015" "2016" "2017"
#calculating the median years
cost1<- cost[,-c(1,3,4,5,6,7)]
medianYears <- data.frame()
medianYears
medianYearCol <- cost %>% select(starts_with('X2005'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)
  
medianYearCol$year<- rep(2005, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2006'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2006, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2007'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2007, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2008'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2008, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2009'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2009, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2010'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2010, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2011'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2011, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2012'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2012, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2013'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2013, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2014'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2014, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2015'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2015, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2016'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2016, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)
medianYearCol <- cost %>% select(starts_with('X2017'), RegionName) %>% mutate(Median = rowMedians(as.matrix(select(., -matches("RegionName")))))
medianYearCol <- medianYearCol %>% select(RegionName,Median)

medianYearCol$year<- rep(2017, nrow(medianYearCol))
medianYears<-rbind(medianYears,medianYearCol)


head(medianYearCol)
head(medianYears)

Median Price Change Over 5 Years Upper half(Orange) of line graph highlights regionNames with significant increase in the median cost price. With RegionName = 10003, 10014 & 10011 median cost price increasing by 0.5 Mn in a matter of 5 years (2013-2017).

Lower half of the plot sees little or no increase. In further sections, each zipcode will further analyzed to reason out these trends.

str(cost$RegionName)
##  int [1:25] 10025 10023 10128 10011 10003 11201 11234 10314 11215 10028 ...
RegionName <- as.numeric(cost$RegionName)
medianPriceAll <-
  medianYears %>% filter(year > 2012) %>% ggplot(aes(
    x = year,
    y = Median,
    
    group = RegionName,
    colour = RegionName
  )) + geom_line() + geom_point() + scale_y_continuous(labels = scales::comma)
ggplotly(medianPriceAll)

Median Price Change Over 2 Years Growth/Increase in median cost price measured for recent 2 consecutive years (2016,2017) shares a different story. The market has been dry with little or no fluctuation. Median Cost for regionName - 10003 which had an upward trend for over 4-5 years (2013-2016) has dropped by 0.1 mn. Rest of the regionNames have little or no increase.

medianPriceAll <-
  medianYears %>% filter(year > (2014)) %>% ggplot(aes(
    x = year,
    y = Median,
    group = RegionName,
    colour = RegionName
  )) + geom_line() + geom_point() + scale_y_continuous(labels = scales::comma)
ggplotly(medianPriceAll)

Clean Cost Data From the above analysis, it is safe to assume that Median Cost Price for Homes have rather not a significant increment the past 2 years.To reduce the complexicity of data in hand, median cost price for year (2017) is chosen to be the actual price of the individual

2 bedroom properties across every regionName(zipcode) is filtered based on the requirement. Decisions made here on will be based on this assumption. Choosing the Last One Year Median Price

currentMedianPrice <- medianYears %>% filter(year == 2017)


cost <-
  cost %>% select(RegionName, CountyName, SizeRank) %>% inner_join(currentMedianPrice, by = "RegionName")
cost <- rename(cost, cost = Median)

kable(head(cost))  %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")
RegionName CountyName SizeRank cost year
10025 New York 1 1342900 2017
10023 New York 3 1988700 2017
10128 New York 14 1622500 2017
10011 New York 15 2354000 2017
10003 New York 21 2005500 2017
11201 Kings 32 1400200 2017

Revenue Data (Airbnb Data) Revenue data contains a mix of information including details about the properties like address, bedrooms, zipcode ,bathrooms to information about host, daily/weekly and monthly price details for stay.

kable(head(revenue))  %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")
id listing_url scrape_id last_scraped name summary space description experiences_offered neighborhood_overview notes transit access interaction house_rules thumbnail_url medium_url picture_url xl_picture_url host_id host_url host_name host_since host_location host_about host_response_time host_response_rate host_acceptance_rate host_is_superhost host_thumbnail_url host_picture_url host_neighbourhood host_listings_count host_total_listings_count host_verifications host_has_profile_pic host_identity_verified street neighbourhood neighbourhood_cleansed neighbourhood_group_cleansed city state zipcode market smart_location country_code country latitude longitude is_location_exact property_type room_type accommodates bathrooms bedrooms beds bed_type amenities square_feet price weekly_price monthly_price security_deposit cleaning_fee guests_included extra_people minimum_nights maximum_nights minimum_minimum_nights maximum_minimum_nights minimum_maximum_nights maximum_maximum_nights minimum_nights_avg_ntm maximum_nights_avg_ntm calendar_updated has_availability availability_30 availability_60 availability_90 availability_365 calendar_last_scraped number_of_reviews number_of_reviews_ltm first_review last_review review_scores_rating review_scores_accuracy review_scores_cleanliness review_scores_checkin review_scores_communication review_scores_location review_scores_value requires_license license jurisdiction_names instant_bookable is_business_travel_ready cancellation_policy require_guest_profile_picture require_guest_phone_verification calculated_host_listings_count calculated_host_listings_count_entire_homes calculated_host_listings_count_private_rooms calculated_host_listings_count_shared_rooms reviews_per_month
2539 https://www.airbnb.com/rooms/2539 2.019071e+13 2019-07-09 Clean & quiet apt home by the park Renovated apt home in elevator building. Spacious, renovated, and clean apt home, one block to F train, 25 minutes to lower Manhatten Renovated apt home in elevator building. Spacious, renovated, and clean apt home, one block to F train, 25 minutes to lower Manhatten Close to Prospect Park and Historic Ditmas Park Very close to F and G trains and Express bus into NY. The B and Q are closeby also. If this room is unavailable on your desired dates, check out our other rooms, such as: https://www.airbnb.com/rooms/10267242 none Close to Prospect Park and Historic Ditmas Park If this room is unavailable on your desired dates, check out our other rooms, such as: https://www.airbnb.com/rooms/10267242 Very close to F and G trains and Express bus into NY. The B and Q are closeby also. -The security and comfort of all our guests is important to us! Therefore, no one is permitted to check in without first emailing a clear government issued photo ID, acceptable to manager. Instructions will be provided in the house manual. -No eating, drinking or storage of food in the room. -No smoking. -Illicit drug use is strictly forbidden. -Quiet hours after 10pm and before 8am, This is respectful for our neighbors and other guests. -Please clean up after yourself when using the kitchen and bath. -Please lock the doors and close the windows when exiting the home. -Please indicate and pay for the correct number of guests. Failure to do so will cancel your reservation with no refund due. -Please remove your shoes when entering the apt home. Thanks for choosing our home! NA NA https://a0.muscache.com/im/pictures/3949d073-a02e-4ebc-aa9c-ac74f00eaa1f.jpg?aki_policy=large NA 2787 https://www.airbnb.com/users/show/2787 John 2008-09-07 New York, New York, United States Educated professional living in Brooklyn. I love meeting new people, running, hiking, fine foods, traveling, etc. One of my favorite trips was spending New Year’s Eve in London on the Thames River. Big Ben, spectacular fireworks and light show; and fun times with a good crowd of international tourists. A most memorable night and trip! Also, I generally approach life with a positive attitude. I look forward to meeting you. within an hour 100% N/A f https://a0.muscache.com/im/pictures/8674565a-758d-476b-a580-4d99ea9baab9.jpg?aki_policy=profile_small https://a0.muscache.com/im/pictures/8674565a-758d-476b-a580-4d99ea9baab9.jpg?aki_policy=profile_x_medium Gravesend 6 6 [‘email’, ‘phone’, ‘reviews’, ‘kba’] t t Brooklyn , NY, United States Brooklyn Kensington Brooklyn Brooklyn NY 11218 New York Brooklyn , NY US United States 40.64749 -73.97237 f Apartment Private room 2 1 1 1 Real Bed {TV,“Cable TV”,Internet,Wifi,“Wheelchair accessible”,Kitchen,“Free parking on premises”,Elevator,“Free street parking”,“Buzzer/wireless intercom”,Heating,“Suitable for events”,Washer,Dryer,“Smoke detector”,“Carbon monoxide detector”,“First aid kit”,“Safety card”,“Fire extinguisher”,Essentials,Shampoo,“24-hour check-in”,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“translation missing: en.hosting_amenity_49”,“translation missing: en.hosting_amenity_50”,“Self check-in”,Keypad,“Outlet covers”,“Hot water”,“Bed linens”,“Extra pillows and blankets”,Microwave,“Coffee maker”,Refrigerator,“Dishes and silverware”,“Cooking basics”,Oven,Stove,“Luggage dropoff allowed”,“Long term stays allowed”,“Cleaning before checkout”} NA $149.00 $299.00 $999.00 $100.00 $25.00 1 $35.00 1 730 1 1 730 730 1 730 3 weeks ago t 30 60 90 365 2019-07-09 9 2 2015-12-04 2018-10-19 98 10 10 10 10 10 10 f f f moderate f f 6 0 5 1 0.21
2595 https://www.airbnb.com/rooms/2595 2.019071e+13 2019-07-09 Skylit Midtown Castle Find your romantic getaway to this beautiful, spacious skylit studio in the heart of Midtown, Manhattan. STUNNING SKYLIT STUDIO / 1 BED + SINGLE / FULL BATH / FULL KITCHEN / FIREPLACE / CENTRALLY LOCATED / WiFi + APPLE TV / SHEETS + TOWELS
  • Spacious (500+ft²), immaculate and nicely furnished & designed studio. - Tuck yourself into the ultra comfortable bed under the skylight. Fall in love with a myriad of bright lights in the city night sky. - Single-sized bed/convertible floor mattress with luxury bedding (available upon request). - Gorgeous pyramid skylight with amazing diffused natural light, stunning architectural details, soaring high vaulted ceilings, exposed brick, wood burning fireplace, floor seating area with natural zafu cushions, modern style mixed with eclectic art & antique treasures, large full bath, newly renovated kitchen, air conditioning/heat, high speed WiFi Internet, and Apple TV. - Centrally located in the heart of Midtown Manhattan just a few blocks from all subway connections in the very desirable Midtown location a few minutes walk to Times Square, the Theater District, Bryant Park and Herald Square. - The Midtown Castle is a uniquely charming Dutch Colonial survivor from the 1890s. - This is
Find your romantic getaway to this beautiful, spacious skylit studio in the heart of Midtown, Manhattan. STUNNING SKYLIT STUDIO / 1 BED + SINGLE / FULL BATH / FULL KITCHEN / FIREPLACE / CENTRALLY LOCATED / WiFi + APPLE TV / SHEETS + TOWELS - Spacious (500+ft²), immaculate and nicely furnished & designed studio. - Tuck yourself into the ultra comfortable bed under the skylight. Fall in love with a myriad of bright lights in the city night sky. - Single-sized bed/convertible floor mattress with luxury bedding (available upon request). - Gorgeous pyramid skylight with amazing diffused natural light, stunning architectural details, soaring high vaulted ceilings, exposed brick, wood burning fireplace, floor seating area with natural zafu cushions, modern style mixed with eclectic art & antique treasures, large full bath, newly renovated kitchen, air conditioning/heat, high speed WiFi Internet, and Apple TV. - Centrally located in the heart of Midtown Manhattan just a few blocks from all s none Centrally located in the heart of Manhattan just a few blocks from all subway connections in the very desirable Midtown location a few minutes walk to Times Square, the Theater District, Bryant Park and Herald Square. Apartment is located on 37th Street between 5th & 6th Avenue, just a few blocks from all subway connections. Closest Subways (in order of proximity to apartment (Website hidden by Airbnb) W: 34th Street & 6th Avenu (Website hidden by Airbnb) 3: 34th Street & 7th Avenue 7: 42nd & 5th Avenu (Website hidden by Airbnb) S: 42nd Street between Park & Lexington Avenue (Website hidden by Airbnb) E: 34th Street and 8th Avenue If coming by car, there is a parking garage on the block and free street parking. Guests have full access to the kitchen, bathroom and living spaces. The closets are private/off limits. I am a Sound Therapy Practitioner and Kundalini Yoga & Meditation teacher. I work with energy and sound for relaxation and healing, using Symphonic gong, singing bowls, tuning forks, drums, voice and other instruments. Sound relaxation sessions and/or personalized Kundalini Yoga sessions are available in the space upon request. Individual, couples or group sessions available. Licensed acupuncture and massage also available upon request. Please inquire. I welcome my guests at the apartment for check-in, or alternatively, a self check-in can be arranged. Once you are settled in, I am just a phone call, text or email away, should you have any questions, concerns or issues during your stay. My desire is that you have a smooth arrival and amazing stay here. Make yourself at home, respect the space and the neighbors. No pets, no smoking and no unauthorized guests. NA NA https://a0.muscache.com/im/pictures/f0813a11-40b2-489e-8217-89a2e1637830.jpg?aki_policy=large NA 2845 https://www.airbnb.com/users/show/2845 Jennifer 2008-09-09 New York, New York, United States A New Yorker since 2000! My passion is creating beautiful, unique spaces where unforgettable memories are made. It’s my pleasure to host people from around the world and meet new faces. Welcome travelers! I am a Sound Therapy Practitioner and Kundalini Yoga & Meditation teacher. I work with energy and sound for relaxation and healing, using Symphonic gong, singing bowls, tuning forks, drums, voice and other instruments. within a few hours 87% N/A f https://a0.muscache.com/im/users/2845/profile_pic/1259095067/original.jpg?aki_policy=profile_small https://a0.muscache.com/im/users/2845/profile_pic/1259095067/original.jpg?aki_policy=profile_x_medium Midtown 5 5 [‘email’, ‘phone’, ‘reviews’, ‘kba’, ‘work_email’] t t New York, NY, United States Manhattan Midtown Manhattan New York NY 10018 New York New York, NY US United States 40.75362 -73.98377 f Apartment Entire home/apt 2 1 0 1 Real Bed {TV,Wifi,“Air conditioning”,Kitchen,“Paid parking off premises”,“Free street parking”,“Indoor fireplace”,Heating,“Family/kid friendly”,“Smoke detector”,“Carbon monoxide detector”,“Fire extinguisher”,Essentials,Shampoo,“Lock on bedroom door”,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“Self check-in”,Keypad,“Private living room”,Bathtub,“Hot water”,“Bed linens”,“Extra pillows and blankets”,“Ethernet connection”,“Coffee maker”,Refrigerator,“Dishes and silverware”,“Cooking basics”,Oven,Stove,“Luggage dropoff allowed”,“Long term stays allowed”,“Cleaning before checkout”,“Wide entrance for guests”,“Flat path to guest entrance”,“Well-lit path to entrance”,“No stairs or steps to enter”} NA $225.00 $1,995.00 $350.00 $100.00 2 $0.00 1 1125 1 1 1125 1125 1 1125 4 days ago t 25 55 80 355 2019-07-09 45 11 2009-11-21 2019-05-21 95 10 9 10 10 10 9 f f f strict_14_with_grace_period t t 2 1 0 1 0.38
3647 https://www.airbnb.com/rooms/3647 2.019071e+13 2019-07-08 THE VILLAGE OF HARLEM….NEW YORK ! WELCOME TO OUR INTERNATIONAL URBAN COMMUNITY This Spacious 1 bedroom is with Plenty of Windows with a View……. Sleeps…..Four Adults…..two in the Livingrm. with (2) Sofa-beds. (Website hidden by Airbnb) two in the Bedrm.on a very Comfortable Queen Size Bed… A Complete Bathrm…..With Shower and Bathtub……. Fully Equipped with Linens & Towels…….. Spacious Living Room……Flat ScreenTelevision…..DVD Player with Movies available for your viewing during your stay………………………………………………………………….. Dining Area…..for Morning Coffee or Tea…………………………………………….. The Kitchen Area is Modern with Granite Counter Top… includes the use of a Coffee Maker…Microwave to Heat up a Carry Out/In Meal…. Not suited for a Gourmet Cook…or Top Chef……Sorry!!!! . This Flat is located in HISTORIC HARLEM…. near the Appollo Theater and The Museum Mile…on Fifth Avenue. Sylvia’s World Famous Resturant…loca WELCOME TO OUR INTERNATIONAL URBAN COMMUNITY This Spacious 1 bedroom is with Plenty of Windows with a View……. Sleeps…..Four Adults…..two in the Livingrm. with (2) Sofa-beds. (Website hidden by Airbnb) two in the Bedrm.on a very Comfortable Queen Size Bed… A Complete Bathrm…..With Shower and Bathtub……. Fully Equipped with Linens & Towels…….. Spacious Living Room……Flat ScreenTelevision…..DVD Player with Movies available for your viewing during your stay………………………………………………………………….. Dining Area…..for Morning Coffee or Tea…………………………………………….. The Kitchen Area is Modern with Granite Counter Top… includes the use of a Coffee Maker…Microwave to Heat up a Carry Out/In Meal…. Not suited for a Gourmet Cook…or Top Chef……Sorry!!!! . This Flat is located in HISTORIC HARLEM…. near the Appollo Theater and The Museum Mile…on Fifth Avenue. Sylvia’s World Famous Resturant…loca none Upon arrival please have a legibile copy of your Passport and / or State Photo. ID. as well as your confirmation letter. Please NO SMOKING …LOUD TALKING or PARTIES of any kind. Security Deposit and Cleaning Fees in CASH at time of arrival. Security deposit will be refunded within 72hrs pending no damages. .Cleaning fees are non-refundable. At Check Out… Please dispose of all trash/garbage in the bins located outside behind the entrance stairs. Please place all dirty linens and towels in the dirty laundry bin. Please don’t leave any dishes in the sink and dispose of all Plastic Dishes/Plastic Culinary. If you need a late check-out please contact the Emergency Contact Telephone Numbers that are listed in the Flat. PLEASE LEAVE ALL KEYS IN THE FLAT and BE CERTAIN THE DOORS ARE LOCKED. WE HOPE YOU ENJOYED YOUR STAY and will write positve comments and will visit again. NA NA https://a0.muscache.com/im/pictures/838341/9b3c66f3_original.jpg?aki_policy=large NA 4632 https://www.airbnb.com/users/show/4632 Elisabeth 2008-11-25 New York, New York, United States Make Up Artist National/ (Website hidden by Airbnb) Production. I m curently working with a Production Company in WDC and Coordinated a “Day Of Service” for the Presidential Innaugration 2013. I can’t live without Starbucks and Oprah’s Chai Tea Latte…and my circle of of great friends world wide. “BLESS ME INTO USEFULLNESS” is my daily prayer. I within a day 100% N/A f https://a0.muscache.com/im/users/4632/profile_pic/1328402497/original.jpg?aki_policy=profile_small https://a0.muscache.com/im/users/4632/profile_pic/1328402497/original.jpg?aki_policy=profile_x_medium Harlem 1 1 [‘email’, ‘phone’, ‘google’, ‘reviews’, ‘jumio’, ‘government_id’] t t New York, NY, United States Harlem Harlem Manhattan New York NY 10027 New York New York, NY US United States 40.80902 -73.94190 t Apartment Private room 2 1 1 1 Pull-out Sofa {“Cable TV”,Internet,Wifi,“Air conditioning”,Kitchen,“Buzzer/wireless intercom”,Heating,“Smoke detector”,“Carbon monoxide detector”,“translation missing: en.hosting_amenity_49”,“translation missing: en.hosting_amenity_50”} NA $150.00 $200.00 $75.00 2 $20.00 3 7 3 3 7 7 3 7 34 months ago t 30 60 90 365 2019-07-08 0 0 NA NA NA NA NA NA NA f f f strict_14_with_grace_period t t 1 0 1 0 NA
3831 https://www.airbnb.com/rooms/3831 2.019071e+13 2019-07-09 Cozy Entire Floor of Brownstone Urban retreat: enjoy 500 s.f. floor in 1899 brownstone, with wood and ceramic flooring throughout (completed Aug. 2015 through Sept. 2015), roomy bdrm, & upgraded kitchen & bathroom (completed Oct. 2015). It’s sunny and loaded with everything you need! Greetings! We own a double-duplex brownstone in Clinton Hill on Gates near Classon Avenue - (7 blocks to C train, 5 blocks to G train, minutes to all), in which we host on the entire top floor of the upper duplex. This is more of an efficiency set-up: it is the top floor on a two-family, double duplex brownstone. The top floor for our guests consists of a sizable bedroom, full bath and eat-in kitchen for your exclusive use. Our family occupies the floors below. You go through a common hallway and staircase, to get to the top floor (2 easy flights up from the main entrance), but not through any rooms, so it is a fairly private set-up. - Clinton Hill, Gates Avenue near Classon Ave. (1 mi. or less to Williamsburg, Park Slope, Prospect Heights, downtown, Ft. Greene, Bed-Stuy, Bushwick; 20 mins to Manhattan) - includes FiOS, heat (or A/C), hot water, and electricity all included - furnished with two twin beds (convertible into a king bed), one rollaway twin bed and one inflatable Urban retreat: enjoy 500 s.f. floor in 1899 brownstone, with wood and ceramic flooring throughout (completed Aug. 2015 through Sept. 2015), roomy bdrm, & upgraded kitchen & bathroom (completed Oct. 2015). It’s sunny and loaded with everything you need! Greetings! We own a double-duplex brownstone in Clinton Hill on Gates near Classon Avenue - (7 blocks to C train, 5 blocks to G train, minutes to all), in which we host on the entire top floor of the upper duplex. This is more of an efficiency set-up: it is the top floor on a two-family, double duplex brownstone. The top floor for our guests consists of a sizable bedroom, full bath and eat-in kitchen for your exclusive use. Our family occupies the floors below. You go through a common hallway and staircase, to get to the top floor (2 easy flights up from the main entrance), but not through any rooms, so it is a fairly private set-up. - Clinton Hill, Gates Avenue near Classon Ave. (1 mi. or less to Williamsburg, Park Slope, Pro none Just the right mix of urban center and local neighborhood; close to all but enough quiet for a calming walk. B52 bus for a 10-minute ride to downtown Brooklyn is a few yards away on the corner; G train/Classon Avenue is 5 blocks away; C train is about 6 blocks to either the Clinton/Washington stop or Franklin Avenue stop. There is on-street parking, alternate side is twice per week on the immediate block but only once per week on Classon. From LaGuardia Airport, a taxi will cost $30-$35, but there is also a bus that will put you at the Jackson Heights subway station, and from there it’s about 5 stops to catch the G train, which stops 5 blocks away. From JFK, the taxi is closer to $40, but the AirTran can get you conveniently to the A/C line and the C train is about 6 blocks from here. From JFK via subway/metro/train: From JFK take the AirTrain to Howard Beach to catch the A train toward Brooklyn/Manhattan. Take the A train to Utica Avenue and go across that same platform to catch the C local train (you could also transfer at Nostrand but you would have to carry luggage downstairs to cat You will have exclusive use of and access to: a sizable private room as described in “The Space” section, furnished with two twin beds (which we will combine into one king bed upon request) and optional rollaway twin and/or inflatable beds, and other small furnishings; full private bath and private eat-in kitchen both renovated in Fall 2015; sizable dining table in sun-filled kitchen area doubles as a great desk space; alcove perfect for vertical bike storage. Upon request you may also have some use of the livingroom on the floor just below. We’ll be around, but since you have the top floor to yourself, most of the interaction is on the way in or out - we’re open to socializing and did so frequently with our last long-term guests, so it’s really up to you Smoking - outside please; pets allowed but please contact me first for arrangements NA NA https://a0.muscache.com/im/pictures/e49999c2-9fd5-4ad5-b7cc-224deac989aa.jpg?aki_policy=large NA 4869 https://www.airbnb.com/users/show/4869 LisaRoxanne 2008-12-07 New York, New York, United States Laid-back bi-coastal actor/professor/attorney. within a few hours 93% N/A f https://a0.muscache.com/im/users/4869/profile_pic/1371927771/original.jpg?aki_policy=profile_small https://a0.muscache.com/im/users/4869/profile_pic/1371927771/original.jpg?aki_policy=profile_x_medium Clinton Hill 1 1 [‘email’, ‘phone’, ‘reviews’, ‘kba’] t t Brooklyn, NY, United States Brooklyn Clinton Hill Brooklyn Brooklyn NY 11238 New York Brooklyn, NY US United States 40.68514 -73.95976 t Guest suite Entire home/apt 3 1 1 4 Real Bed {TV,“Cable TV”,Internet,Wifi,“Air conditioning”,Kitchen,“Pets allowed”,“Free street parking”,Heating,“Family/kid friendly”,“Smoke detector”,“Carbon monoxide detector”,“Fire extinguisher”,Essentials,Shampoo,“Lock on bedroom door”,“24-hour check-in”,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“Self check-in”,Lockbox,Bathtub,“High chair”,“Stair gates”,“Children’s books and toys”,“Pack ’n Play/travel crib”,“Hot water”,“Luggage dropoff allowed”,“Long term stays allowed”} 500 $89.00 $575.00 $2,100.00 $500.00 1 $0.00 1 730 1 1 730 730 1 730 today t 0 0 3 194 2019-07-09 270 69 2014-09-30 2019-07-05 90 10 9 10 10 10 9 f f f moderate f f 1 1 0 0 4.64
5022 https://www.airbnb.com/rooms/5022 2.019071e+13 2019-07-08 Entire Apt: Spacious Studio/Loft by central park Loft apartment with high ceiling and wood flooring located 10 minutes away from Central Park in Harlem - 1 block away from 6 train and 3 blocks from 2 & 3 line. This is in a recently renovated building which includes elevator, trash shoot. marble entrance and laundromat in the basement. The apartment is a spacious loft studio. The seating area and sleeping area is divided by a bookcase. There is a long hallway entrance where the bathroom and closet for your clothes is situated. The apartment is in mint condition, the walls have been freshly painted a few months ago. Supermarket, and 24 hour convenience store less than 1 block away. 1 block away from Hot Yoga Studio and NY Sports club facility. Perfect for anyone wanting to stay in Manhattan but get more space. 10 minutes away from midtown and 15 minutes away from downtown. The neighborhood is lively and diverse. You will need to travel at least 10 blocks to find cafe’s, restaurants etc.. There are a few restaurants on 100 street on Loft apartment with high ceiling and wood flooring located 10 minutes away from Central Park in Harlem - 1 block away from 6 train and 3 blocks from 2 & 3 line. This is in a recently renovated building which includes elevator, trash shoot. marble entrance and laundromat in the basement. The apartment is a spacious loft studio. The seating area and sleeping area is divided by a bookcase. There is a long hallway entrance where the bathroom and closet for your clothes is situated. The apartment is in mint condition, the walls have been freshly painted a few months ago. Supermarket, and 24 hour convenience store less than 1 block away. 1 block away from Hot Yoga Studio and NY Sports club facility. Perfect for anyone wanting to stay in Manhattan but get more space. 10 minutes away from midtown and 15 minutes away from downtown. The neighborhood is lively and diverse. You will need to travel at least 10 blocks to find cafe’s, restaurants etc.. There are a few restaurants on 100 street on none Please be considerate when staying in the apartment. This is a low key building and it’s important guest are respectful. You can come and go as you please I just ask that you keep a low profile. 1) Please be respectful of neighbors - no loud music after 10pm and keep a low profile 2) Do not open the door for anyone 3) Please keep the apt clean 4) No access to mailbox - please forward personal mail to job or school NA NA https://a0.muscache.com/im/pictures/feb453bd-fdec-405c-8bfa-3f6963d827e9.jpg?aki_policy=large NA 7192 https://www.airbnb.com/users/show/7192 Laura 2009-01-29 Miami, Florida, United States I have been a NYer for almost 10 years. I came to NY to study and never left. I work in the advertising industry and love to eat peanut butter & jelly sandwiches. N/A N/A N/A f https://a0.muscache.com/im/users/7192/profile_pic/1325651676/original.jpg?aki_policy=profile_small https://a0.muscache.com/im/users/7192/profile_pic/1325651676/original.jpg?aki_policy=profile_x_medium East Harlem 1 1 [‘email’, ‘phone’, ‘facebook’, ‘reviews’, ‘kba’] t t New York, NY, United States East Harlem East Harlem Manhattan New York NY 10029 New York New York, NY US United States 40.79851 -73.94399 t Apartment Entire home/apt 1 1 NA 1 Real Bed {Internet,Wifi,“Air conditioning”,Kitchen,Elevator,“Free street parking”,“Buzzer/wireless intercom”,Heating,Washer,Dryer,“Smoke detector”,“Carbon monoxide detector”,Essentials,Shampoo,“Hair dryer”,“Hot water”,“Host greets you”} NA $80.00 $600.00 $1,600.00 $100.00 $80.00 1 $20.00 10 120 10 10 120 120 10 120 3 months ago t 0 0 0 0 2019-07-08 9 4 2012-03-20 2018-11-19 93 10 9 10 10 9 10 f f f strict_14_with_grace_period t t 1 1 0 0 0.10
5099 https://www.airbnb.com/rooms/5099 2.019071e+13 2019-07-08 Large Cozy 1 BR Apartment In Midtown East My large 1 bedroom apartment is true New York City living. The apt is in midtown on the east side and centrally located, just a 10-minute walk from Grand Central Station, Empire State Building, Times Square. The kitchen and living room are large and bright with Apple TV. I have a new Queen Bed that sleeps 2 people, and a Queen Aero Bed that can sleep 2 people in the living room. The apartment is located on the 5th floor of a walk up - no elevator (lift). I have a large 1 bedroom apartment centrally located in Midtown East. A 10 minute walk from Grand Central Station, Times Square, Empire State Building and all major subway and bus lines. The apartment is located on the 5th floor of a pre-war walk up building-no elevator/lift. The apartment is bright with has high ceilings and flow through rooms. A spacious, cozy living room with Netflix and Apple TV. A large bright kitchen to sit and enjoy coffee or tea. The bedroom is spacious with a comfortable queen size bed that sleeps 2. I have a comfortable queen size aero bed that fits in the living room and sleeps 2. It can be tucked away for living space and opened when ready for bed. I’d be happy to give you tips and advice on the best ways to experience the most of NYC. The apartment’s location is great for sightseeing. ** Check out my listing guidebook ** If you would like to stay local in the area, there is a very long & famous strip of bars and restaurants along 3rd Avenue, which My large 1 bedroom apartment is true New York City living. The apt is in midtown on the east side and centrally located, just a 10-minute walk from Grand Central Station, Empire State Building, Times Square. The kitchen and living room are large and bright with Apple TV. I have a new Queen Bed that sleeps 2 people, and a Queen Aero Bed that can sleep 2 people in the living room. The apartment is located on the 5th floor of a walk up - no elevator (lift). I have a large 1 bedroom apartment centrally located in Midtown East. A 10 minute walk from Grand Central Station, Times Square, Empire State Building and all major subway and bus lines. The apartment is located on the 5th floor of a pre-war walk up building-no elevator/lift. The apartment is bright with has high ceilings and flow through rooms. A spacious, cozy living room with Netflix and Apple TV. A large bright kitchen to sit and enjoy coffee or tea. The bedroom is spacious with a comfortable queen size bed that sleeps 2. I none My neighborhood in Midtown East is called Murray Hill. The area is very centrally located with easy access to explore . The apartment is about 5 blocks (7 minute walk) to the United Nations and Grand Central Station the main and most historic train station. Grand Central will give you access to every train in the city. The apartment is also very close to main attractions, It’s about a 10 minute walk to both the Empire State Building and Times Square. There’s a great shopping area with dozens of stores including H&M, Zara, The Gap, BeBe and the world famous Macy’s department store. These shops are a 10 minute walk up East 34th Street from 5th avenue and 8th avenue. If you would like to stay local in the area, there is a very long & famous strip of bars and restaurants along 3rd avenue, which is just around the corner from the apartment. It’s commonly known as the 3rd avenue strip. Read My Full Listing For All Information. New York City really is the city that doesn’t sleep. There’s a constant flow of people, bikes and cars. The city can be noisy at times, if you’re a light sleeper, ear plugs would help. Check out my local guide book for things to do. From the apartment is a 10 minute walk to Grand Central Station on East 42nd Street, a 10 minute walk to the Empire State Building on East 34th Street and 5th Avenue, a 10 minute walk to Times Square on West 42 Street and about 20 minutes walk to Central Park on 59th Street. Depending on how long you are staying, I would recommend a 7 day unlimited metro card. This allows you to travel unlimited all day and night on any train or bus in and outside of the city. Grand Central Station is the main NYC train station. You can find any train connection and get anywhere in Manhattan from Grand Central Station. You can get to Brooklyn, Queens, The Bronx and Staten Island from Grand Central Station The M15 bus is around the corner on 2nd avenue. This bus will take you from uptown Harlem to the East Village and South Street Seaport. It will also take you to the Staten Island ferry and Statue of Liberty and everywhere in between along the east side. The M101 bus is just up the street on 3rd aven I will meet you upon arrival. I usually check in with guests via text or email. I’m available by text, email or phone call with any questions, suggestions or to help out. • Check-in time is 2PM. • Check-out time is 12 PM. Please be respectful of the space and leave the apartment in the condition you were welcomed into. NA NA https://a0.muscache.com/im/pictures/0790b1a5-8981-41cc-a370-fa2b982a8803.jpg?aki_policy=large NA 7322 https://www.airbnb.com/users/show/7322 Chris 2009-02-02 New York, New York, United States I’m an artist, writer, traveler, and a native new yorker. Welcome to my city. within an hour 100% N/A f https://a0.muscache.com/im/pictures/user/26745d24-d818-4bf5-8f9e-26b097121ba7.jpg?aki_policy=profile_small https://a0.muscache.com/im/pictures/user/26745d24-d818-4bf5-8f9e-26b097121ba7.jpg?aki_policy=profile_x_medium Flatiron District 1 1 [‘email’, ‘phone’, ‘reviews’, ‘jumio’, ‘government_id’] t f New York, NY, United States Midtown East Murray Hill Manhattan New York NY 10016 New York New York, NY US United States 40.74767 -73.97500 f Apartment Entire home/apt 2 1 1 1 Real Bed {TV,“Cable TV”,Internet,Wifi,Kitchen,“Buzzer/wireless intercom”,Heating,“Smoke detector”,“Carbon monoxide detector”,“Fire extinguisher”,Essentials,Shampoo,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“translation missing: en.hosting_amenity_49”,“translation missing: en.hosting_amenity_50”,“Hot water”,“Bed linens”,“Extra pillows and blankets”,“Host greets you”} NA $200.00 $300.00 $125.00 2 $100.00 3 21 3 3 21 21 3 21 3 weeks ago t 23 48 48 129 2019-07-08 74 9 2009-04-20 2019-06-22 89 10 9 10 10 9 9 f f f strict_14_with_grace_period t t 1 1 0 0 0.59

Data Quality Check

Missing Values Majority of description columns and host related information are blank. Steps are taken in the following section to filter columns with higher percentage of Nulls/NA.

It is also important how we handle missing value for example based on the variable and number of missing values we can either remove the missing values , or depending on the requirement we can impute the missing values by 0 or mean , median , mode , regression etc.

numMissingVal <-sapply(revenue, function(x) sum(length(which(is.na(x)))))  
kable(as.data.frame(numMissingVal)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")
numMissingVal
id 0
listing_url 0
scrape_id 0
last_scraped 0
name 0
summary 0
space 0
description 0
experiences_offered 0
neighborhood_overview 0
notes 0
transit 0
access 0
interaction 0
house_rules 0
thumbnail_url 48895
medium_url 48895
picture_url 0
xl_picture_url 48895
host_id 0
host_url 0
host_name 0
host_since 0
host_location 0
host_about 0
host_response_time 0
host_response_rate 0
host_acceptance_rate 0
host_is_superhost 0
host_thumbnail_url 0
host_picture_url 0
host_neighbourhood 0
host_listings_count 21
host_total_listings_count 21
host_verifications 0
host_has_profile_pic 0
host_identity_verified 0
street 0
neighbourhood 0
neighbourhood_cleansed 0
neighbourhood_group_cleansed 0
city 0
state 0
zipcode 0
market 0
smart_location 0
country_code 0
country 0
latitude 0
longitude 0
is_location_exact 0
property_type 0
room_type 0
accommodates 0
bathrooms 56
bedrooms 22
beds 40
bed_type 0
amenities 0
square_feet 48487
price 0
weekly_price 0
monthly_price 0
security_deposit 0
cleaning_fee 0
guests_included 0
extra_people 0
minimum_nights 0
maximum_nights 0
minimum_minimum_nights 0
maximum_minimum_nights 0
minimum_maximum_nights 0
maximum_maximum_nights 0
minimum_nights_avg_ntm 0
maximum_nights_avg_ntm 0
calendar_updated 0
has_availability 0
availability_30 0
availability_60 0
availability_90 0
availability_365 0
calendar_last_scraped 0
number_of_reviews 0
number_of_reviews_ltm 0
first_review 0
last_review 0
review_scores_rating 11022
review_scores_accuracy 11060
review_scores_cleanliness 11043
review_scores_checkin 11078
review_scores_communication 11055
review_scores_location 11082
review_scores_value 11080
requires_license 0
license 0
jurisdiction_names 0
instant_bookable 0
is_business_travel_ready 0
cancellation_policy 0
require_guest_profile_picture 0
require_guest_phone_verification 0
calculated_host_listings_count 0
calculated_host_listings_count_entire_homes 0
calculated_host_listings_count_private_rooms 0
calculated_host_listings_count_shared_rooms 0
reviews_per_month 10052

Account for Missing Zipcodes Zipcode column contains 611 missing values. Ignoring these values can prove detrimental to the analysis. Zipcodes are imputed by selecting a non-NA value from Neighbourhood Group Cleansed.

Total Number of NA values in Zipcode Column after Imputation:

revenue <-
  revenue %>% group_by(neighbourhood_group_cleansed) %>% fill(zipcode) %>% ungroup()

sum(is.array(revenue$zipcode))
## [1] 0

Dollar-ed Price Columns Value prefix of every price row prevents numeric manipulation. It is thus removed from three columns: Price, Weekly Price & Monthly Price.

revenue$price <- as.numeric(gsub('[$,]','', revenue$price))
revenue$weekly_price <- as.numeric(gsub('[$,]','', revenue$weekly_price))
revenue$monthly_price <- as.numeric(gsub('[$,]','', revenue$monthly_price))
head(revenue[c("price","weekly_price","monthly_price")])

Negative or Zero Valued Columns None of the price columns: Price, weekly price & Monthly price have zero or negative value.

revCharCol <- colnames(revenue %>% ungroup() %>% select_if(is.character))


revZeroNeg <-
  sapply(revenue[,!(names(revenue) %in% revCharCol)], function(x)
    count(x <= 0, na.rm = TRUE))
## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors

## Warning in Ops.factor(x, 0): '<=' not meaningful for factors
kable(as.data.frame(revZeroNeg)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")
revZeroNeg
id 0
listing_url 0
scrape_id 0
last_scraped 0
name 0
summary 0
space 0
description 0
experiences_offered 0
neighborhood_overview 0
notes 0
transit 0
access 0
interaction 0
house_rules 0
thumbnail_url 0
medium_url 0
picture_url 0
xl_picture_url 0
host_id 0
host_url 0
host_name 0
host_since 0
host_location 0
host_about 0
host_response_time 0
host_response_rate 0
host_acceptance_rate 0
host_is_superhost 0
host_thumbnail_url 0
host_picture_url 0
host_neighbourhood 0
host_listings_count 901
host_total_listings_count 901
host_verifications 0
host_has_profile_pic 0
host_identity_verified 0
street 0
neighbourhood 0
neighbourhood_cleansed 0
neighbourhood_group_cleansed 0
city 0
state 0
zipcode 0
market 0
smart_location 0
country_code 0
country 0
latitude 0
longitude 48895
is_location_exact 0
property_type 0
room_type 0
accommodates 0
bathrooms 126
bedrooms 4569
beds 1094
bed_type 0
amenities 0
square_feet 34
price 11
weekly_price 0
monthly_price 0
security_deposit 0
cleaning_fee 0
guests_included 0
extra_people 0
minimum_nights 0
maximum_nights 0
minimum_minimum_nights 0
maximum_minimum_nights 0
minimum_maximum_nights 0
maximum_maximum_nights 0
minimum_nights_avg_ntm 0
maximum_nights_avg_ntm 0
calendar_updated 0
has_availability 0
availability_30 24471
availability_60 20503
availability_90 19357
availability_365 17533
calendar_last_scraped 0
number_of_reviews 10052
number_of_reviews_ltm 19747
first_review 0
last_review 0
review_scores_rating 0
review_scores_accuracy 0
review_scores_cleanliness 0
review_scores_checkin 0
review_scores_communication 0
review_scores_location 0
review_scores_value 0
requires_license 0
license 0
jurisdiction_names 0
instant_bookable 0
is_business_travel_ready 0
cancellation_policy 0
require_guest_profile_picture 0
require_guest_phone_verification 0
calculated_host_listings_count 0
calculated_host_listings_count_entire_homes 21101
calculated_host_listings_count_private_rooms 24149
calculated_host_listings_count_shared_rooms 47205
reviews_per_month 0

check for row duplication

revenue[which(duplicated(revenue) ==T),] 

Data Filtering Inorder to remove columns that add little or no value to the analysis, some of the smart data munging techniques are incorporated. These include removing columns based on pattern matching with their names, imbalanced columns, character columns with 100 % variance etc.

Other variable reduction methods are Principal Component Analysis , Variable clustering based on 1-R2 ratio value we can select the most relevant variables in each cluster.

Account for Zero Variance, Imbalanced, high NA valued and 100 percent variance character columns. Use associated methods to remove these columns from revenue data. Worst case - Manually remove columns keeping the final outcome in mind.

Imbalanced/Zero Variance columns add no value to the analysis. These columns are removed using nearzeroVar method from Caret package.

#Columns Removed:
zvdf <- nearZeroVar(revenue, saveMetrics = TRUE)
ZVnames=rownames(subset(zvdf, nzv== TRUE))
revenue <- revenue[ , !(names(revenue) %in% ZVnames)]

#printlis(ZVnames)

Pattern Matching Column names starting with ???calendar???,???require???, ???host??? and ending with ???url??? and ???nights??? are irrelevant information when the property is invested in, by the real estate company. These columns are removed.

These methods are taken after going through the overall data and based on what we are targeting as our target variable.

Columns Removed:

pattern <-
  colnames(
    revenue %>% select(
      starts_with("require"),
      starts_with("host"),
      starts_with("calendar"),
      ends_with("url"),
      ends_with("nights")
    )
  )
revenue <- revenue[,!(names(revenue) %in% pattern)]

Based on Unique Values - Character Columns Character columns with near 100% variance are removed as they provide no group level information that can be used on a larger population scale.

These columns include textual columns describing the home, host, amenties etc. For lack of conclusion from other variables, these columns can be used for sentimenta analysis.

I have done sentimental analysis using python libraries wherein we have done the following: -We have selected the ID and the summary columns of the Airbnb data for our sentimental analysis. -The summary gives us an idea of the place where the customers would live and to make it appealing for the customers the owners must portray the details in the most effective way possible. -Our sentimental analysis tells us whether the summary provided by the owner for a room/apartment appeals the customers in a positive or a negative way. -The wordcloud shows us the intensity of the words that tells us about the word frequency. So, in this case we have made wordclouds for the positive as well as the negative summary. -This would help the owners to change their way towards the marketing side, so that they can use the most used positive words in order to increase their customer staying at their place resulting in more revenue for themselves

Columns Removed:

We will also drop columns which have more than 50% missing values

nadf <- revenue %>% summarise_all(funs(sum(is.na(.))))
## Warning: funs() is soft deprecated as of dplyr 0.8.0
## please use list() instead
## 
## # Before:
## funs(name = f(.)
## 
## # After: 
## list(name = ~f(.))
## This warning is displayed once per session.
nadf <- nadf %>% gather(key = var_name, value = value, 1:ncol(nadf))
nadf$numNa <- round(nadf$value/nrow(revenue),2)
naval <- nadf %>% filter(numNa > 0.5) %>% pull(var_name)
revenue <- revenue[,!(names(revenue) %in% naval)]

#naval
#revenue
 
dropcol <- c('id',
             'street',
             'city',
             'CountyName',
             'cleaning_fee',
             'guests_included',
             'extra_people',
             'review_scores_communication',
             'review_scores_checkin',
             'review_scores_cleanliness',
             'review_scores_value',
             'reviews_per_month',
             'instant_bookable',
             'cancellation_policy',
             'smart_location',
             'is_location_exact',
             'calculated_host_listings_count',
             'name', 
             'summary', 
             'space', 
             'description', 
             'neighborhood_overview', 
             'notes', 
             'transit', 
             'access', 
             'interaction', 
             'house_rules', 
             'amenities'
             )  

head(dropcol)
## [1] "id"              "street"          "city"            "CountyName"     
## [5] "cleaning_fee"    "guests_included"

dropping all the unwanted columns based on various statistical significance, analyzing PCA and the variance lost if we removed these columns

revenue <- revenue[,!(names(revenue) %in% dropcol)]
dim(revenue) 
## [1] 48895    29

overall the number of variables have reduced to 29 now

Clean Revenue Data

kable(head(revenue))  %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")
last_scraped neighbourhood neighbourhood_cleansed neighbourhood_group_cleansed zipcode latitude longitude property_type room_type accommodates bathrooms bedrooms beds price security_deposit minimum_nights_avg_ntm maximum_nights_avg_ntm availability_30 availability_60 availability_90 availability_365 number_of_reviews number_of_reviews_ltm last_review review_scores_rating review_scores_accuracy review_scores_location calculated_host_listings_count_entire_homes calculated_host_listings_count_private_rooms
2019-07-08 Highbridge Highbridge Bronx 10452 40.83232 -73.93184 House Private room 1 1.0 1 1 40 $0.00 1 2 22 50 78 353 219 86 2019-07-04 98 10 9 0 3
2019-07-08 Highbridge Highbridge Bronx 10452 40.83075 -73.93058 House Private room 2 1.5 1 1 45 $0.00 1 28 21 37 56 323 138 59 2019-06-30 97 10 10 0 3
2019-07-09 The Bronx Clason Point Bronx 10473 40.81309 -73.85514 House Private room 3 1.0 1 2 90 $0.00 2 1125 25 55 82 349 0 0 NA NA NA 2 4
2019-07-09 Eastchester Eastchester Bronx 10475 40.88057 -73.83572 Apartment Entire home/apt 2 1.0 1 1 105 $150.00 2 1125 30 60 90 365 38 14 2019-06-27 98 10 9 6 6
2019-07-08 Kingsbridge Heights Kingsbridge Bronx 10468 40.87207 -73.90193 Apartment Entire home/apt 2 1.0 1 1 90 $0.00 30 330 11 41 71 346 4 4 2019-01-02 90 10 10 1 1
2019-07-09 The Bronx Woodlawn Bronx 10470 40.89747 -73.86390 House Entire home/apt 4 1.0 1 3 77 $100.00 1 9 13 24 47 309 197 51 2019-06-23 92 9 9 1 0

Joining both the clean data based on ZipCOdes and RegionName Revenue and Cost Data are merged based on common regionName/Zipcode. The real estate company has already chosen 2 Bed room homes as profitable venture.

Data is further refined to account for only 2 bed room homes. Dimension of the final data set:

final <- merge(revenue, cost, by.x = "zipcode",by.y = "RegionName")
final <- subset(final,final$bedrooms == '2')
dim(final)
## [1] 1565   33

Exploratory Data Analysis understanding data

kable(head(final))  %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")
zipcode last_scraped neighbourhood neighbourhood_cleansed neighbourhood_group_cleansed latitude longitude property_type room_type accommodates bathrooms bedrooms beds price security_deposit minimum_nights_avg_ntm maximum_nights_avg_ntm availability_30 availability_60 availability_90 availability_365 number_of_reviews number_of_reviews_ltm last_review review_scores_rating review_scores_accuracy review_scores_location calculated_host_listings_count_entire_homes calculated_host_listings_count_private_rooms CountyName SizeRank cost year
2 10003 2019-07-08 Manhattan East Village Manhattan 40.73245 -73.98535 Apartment Entire home/apt 5 1 2 4 300 $500.00 30.0 100 30 60 90 180 0 0 NA NA NA 1 0 New York 21 2005500 2017
4 10003 2019-07-08 Manhattan Gramercy Manhattan 40.73598 -73.98161 Apartment Entire home/apt 4 1 2 3 125 1.0 1125 0 0 0 0 19 0 2016-06-23 88 9 10 1 0 New York 21 2005500 2017
6 10003 2019-07-08 East Village East Village Manhattan 40.72862 -73.98885 Apartment Entire home/apt 5 1 2 2 142 $150.00 1.0 1125 1 10 12 242 214 46 2019-07-05 92 9 10 1 0 New York 21 2005500 2017
24 10003 2019-07-08 Gramercy Park Gramercy Manhattan 40.73268 -73.98399 Apartment Entire home/apt 5 1 2 3 199 $300.00 1.6 1125 11 39 57 72 26 26 2019-06-30 96 10 10 1 0 New York 21 2005500 2017
31 10003 2019-07-08 Manhattan East Village Manhattan 40.73029 -73.98836 Apartment Entire home/apt 3 1 2 2 62 $0.00 4.0 1125 0 0 5 5 4 1 2018-07-08 100 10 10 1 0 New York 21 2005500 2017
32 10003 2019-07-08 Manhattan East Village Manhattan 40.73167 -73.98581 Apartment Entire home/apt 3 1 2 3 140 7.0 38 0 0 0 0 0 0 NA NA NA 1 0 New York 21 2005500 2017

COnverting char to factor Data Type Handling Convert ???Character??? columns to Factors to ease the process of building discrete charts.

final <- final %>% mutate_if(sapply(final,is.character),as.factor)
str(final)
## 'data.frame':    1565 obs. of  33 variables:
##  $ zipcode                                     : Factor w/ 200 levels "","07093","07302",..: 7 7 7 7 7 7 7 7 7 7 ...
##  $ last_scraped                                : Factor w/ 2 levels "2019-07-08","2019-07-09": 1 1 1 1 1 1 1 1 1 1 ...
##  $ neighbourhood                               : Factor w/ 195 levels "","Allerton",..: 106 106 55 72 106 106 55 106 106 106 ...
##  $ neighbourhood_cleansed                      : Factor w/ 221 levels "Allerton","Arden Heights",..: 65 87 65 87 65 65 65 87 87 93 ...
##  $ neighbourhood_group_cleansed                : Factor w/ 5 levels "Bronx","Brooklyn",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ latitude                                    : num  40.7 40.7 40.7 40.7 40.7 ...
##  $ longitude                                   : num  -74 -74 -74 -74 -74 ...
##  $ property_type                               : Factor w/ 36 levels "Aparthotel","Apartment",..: 2 2 2 2 2 2 28 2 2 2 ...
##  $ room_type                                   : Factor w/ 3 levels "Entire home/apt",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ accommodates                                : int  5 4 5 5 3 3 6 4 4 4 ...
##  $ bathrooms                                   : num  1 1 1 1 1 1 2 1 1 2.5 ...
##  $ bedrooms                                    : int  2 2 2 2 2 2 2 2 2 2 ...
##  $ beds                                        : int  4 3 2 3 2 3 4 2 2 2 ...
##  $ price                                       : num  300 125 142 199 62 140 260 239 280 999 ...
##  $ security_deposit                            : Factor w/ 225 levels "","$0.00","$1,000.00",..: 178 1 56 137 2 1 1 2 1 176 ...
##  $ minimum_nights_avg_ntm                      : num  30 1 1 1.6 4 7 3 5 3.3 3 ...
##  $ maximum_nights_avg_ntm                      : num  100 1125 1125 1125 1125 ...
##  $ availability_30                             : int  30 0 1 11 0 0 2 0 0 23 ...
##  $ availability_60                             : int  60 0 10 39 0 0 10 17 0 44 ...
##  $ availability_90                             : int  90 0 12 57 5 0 26 17 0 74 ...
##  $ availability_365                            : int  180 0 242 72 5 0 26 266 0 164 ...
##  $ number_of_reviews                           : int  0 19 214 26 4 0 24 2 0 11 ...
##  $ number_of_reviews_ltm                       : int  0 0 46 26 1 0 24 0 0 2 ...
##  $ last_review                                 : Factor w/ 1765 levels "","2011-03-28",..: 1 666 1762 1757 1400 1 1758 1213 1 1700 ...
##  $ review_scores_rating                        : int  NA 88 92 96 100 NA 96 90 NA 100 ...
##  $ review_scores_accuracy                      : int  NA 9 9 10 10 NA 10 8 NA 10 ...
##  $ review_scores_location                      : int  NA 10 10 10 10 NA 10 10 NA 10 ...
##  $ calculated_host_listings_count_entire_homes : int  1 1 1 1 1 1 2 2 1 1 ...
##  $ calculated_host_listings_count_private_rooms: int  0 0 0 0 0 0 0 1 0 0 ...
##  $ CountyName                                  : Factor w/ 722 levels "Ada","Adams",..: 460 460 460 460 460 460 460 460 460 460 ...
##  $ SizeRank                                    : int  21 21 21 21 21 21 21 21 21 21 ...
##  $ cost                                        : num  2005500 2005500 2005500 2005500 2005500 ...
##  $ year                                        : num  2017 2017 2017 2017 2017 ...

Corrected Price by Room Type: Following are the assumptions made: Price of the daily rental in Revenue data is reflective of the space that is offered and not the entire property itself. The price must be specifically corrected to account for entire property to account the benefit.

Assumption Made: If the property type == Private Room, it is multipled by number of bedrooms to account for overall price. Correction applied is returned to original price column.

final <- final %>% mutate(price = if_else(room_type == "Private room",
                                          price * bedrooms,
                                          price))

final

Missing Values Nothing too alarming, major chunk of columns look okay to proceed with the analysis.

gg_miss_upset(final)

number of properties by neighbourhood

ggplot(final, aes(neighbourhood_group_cleansed)) + geom_bar() + labs(x = "Neighbourhood", y = "Number of Properties") + geom_text(stat='count', aes(label=..count..), vjust= -0.3)  + theme_classic()

number of properties by zipcode

ggplot(final, aes(zipcode, fill = neighbourhood_group_cleansed)) + geom_bar() + labs(x = "Zipcode", y = "Number of Properties") + geom_text(stat='count', aes(label=..count..), vjust= -0.3) + theme(axis.text.x = element_text(angle = 90, hjust = 1))  + theme_bw() + theme(plot.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.border = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1))

Property cost by zipcode

medianCost1 <- final %>% select(zipcode,neighbourhood_group_cleansed, cost) %>% filter(neighbourhood_group_cleansed == c("Manhattan","Brooklyn"))  %>% group_by(neighbourhood_group_cleansed,zipcode) %>% summarise_all(funs(median)) %>% ggplot(aes(x = zipcode, y = cost, fill =neighbourhood_group_cleansed )) + geom_bar(stat = "identity") +scale_y_continuous(labels = scales::comma) + labs(y = "Cost", x = "Zipcode") + theme_bw() + theme(plot.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.border = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1)) + guides(fill = guide_legend(title = "Neighbourhood"))
## Warning in `==.default`(neighbourhood_group_cleansed, c("Manhattan",
## "Brooklyn")): longer object length is not a multiple of shorter object
## length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
medianCost2 <- final %>% select(zipcode,neighbourhood_group_cleansed, cost) %>% filter(neighbourhood_group_cleansed == c("Staten Island","Queens")) %>% group_by(neighbourhood_group_cleansed,zipcode) %>% summarise_all(funs(median)) %>% ggplot(aes(x = zipcode, y = cost, fill =neighbourhood_group_cleansed )) + geom_bar(stat = "identity") +scale_y_continuous(labels = scales::comma) + labs(x = "Cost", y = "Zipcode") + theme_bw() + theme(plot.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.border = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1)) + guides(fill = guide_legend(title = "Neighbourhood"))
## Warning in `==.default`(neighbourhood_group_cleansed, c("Staten Island", :
## longer object length is not a multiple of shorter object length

## Warning in `==.default`(neighbourhood_group_cleansed, c("Staten Island", :
## longer object length is not a multiple of shorter object length
cowplot::plot_grid(medianCost1, medianCost2, labels = "AUTO", ncol = 1, align = 'v')

Property type Vs Cost In order to develop deeper understanding with our data, property cost is measured across different property types: Apartment, House, Loft etc. Based on the analysis we can see the effect a property type has on the cost of it.

propTypeCost <- ggplot(final, aes(x=property_type, y=cost, fill = zipcode)) + scale_y_continuous(labels = scales::comma) + geom_point(aes(col = zipcode, size=cost)) + 
  geom_smooth(method="loess", se=F) + labs( x="Property type", y="Cost") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplotly(propTypeCost)

Types of property rented in nyc

property_df <- final %>%
  filter(property_type %in% c("Apartment","Condominium", "House",
                              "Loft")) %>% 
  group_by(neighbourhood_group_cleansed) %>%
  mutate(total_property = n()) %>%
  group_by(neighbourhood_group_cleansed, property_type) %>%
  mutate(borough_prop_count = n())
property_df2 <-unique(select(property_df, neighbourhood_group_cleansed,
                             property_type, total_property, borough_prop_count)) %>%
  mutate(ratio = borough_prop_count/total_property)

property_df2 <- bind_rows(property_df2, list(neighbourhood_group_cleansed = "Staten Island",property_type = "Condominium", total_property = 0, borough_prop_count = 0, ratio = 0))
## Warning in bind_rows_(x, .id): binding factor and character vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding factor and character vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
property_df2 <- bind_rows(property_df2, list(neighbourhood_group_cleansed = "Staten Island",property_type = "Loft", total_property = 0, borough_prop_count = 0, ratio = 0))


ggplot(property_df2, aes(x = neighbourhood_group_cleansed, y = ratio,
                         fill = property_type)) +
  geom_bar(position = "dodge",stat="identity") + 
  xlab("Boroughs") + ylab("Count") +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual("Property Type",values=c("#357b8a","turquoise3", "#ff5a5f",
                                             "darkseagreen")) +
  ggtitle("Types of Properties Rented in NYC") +
  theme_minimal()+
  theme(text = element_text(family = "Georgia", size = 10, face = "bold"),
        plot.title = element_text(size = 16,color = "#ff5a5f", margin = margin(b = 7)),
        plot.subtitle = element_text(size = 10, color = "darkslategrey", margin = margin(b = 7)))
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database
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Most popular zipcodes

final %>% group_by(zipcode) %>%
  summarise(num = n()) %>%
  arrange() %>%
  top_n(20) %>%
  ggplot(aes(x = reorder(zipcode,num), y = num))+
  geom_bar(stat = "identity", fill = "#00FF00") +
  coord_flip() +
  labs(title = "Most popular zipcodes", 
       subtitle = "zipcodes with most number of Airbnb hosts",
       colour   = " Borough") +  
  xlab("Zipcode") +
  ylab("Number of Airbnb Rentals") +
  theme_minimal()+
  theme(text = element_text(size = 10),
        plot.title = element_text(size = 16,color = "#00FF00", 
                                  face = "bold",margin = margin(b = 7)),
        plot.subtitle = element_text(size = 10, color = "darkslategrey", 
                                     margin = margin(b = 7)))
## Selecting by num

pricebynbghrhood <- ggplot(final,aes(x=price, fill=neighbourhood_group_cleansed)) + geom_density(alpha = 0.3) +
  scale_x_continuous(limits = quantile(final$price, c(0, 0.99))) + labs(x = "Price/Night", y = "Density") + 
  guides(fill = guide_legend(title = "Neighbourhood")) 
ggplotly(pricebynbghrhood)
## Warning: Removed 16 rows containing non-finite values (stat_density).

Revenue - Price per property by Zipcode - It is important to find effect of zipcode on prices Going back to listing zipcodes ,prices/Night have too many close contenders. We will be plotting and finding out the most effective zipcodes where the prices are high

pricebyZipcode <- ggplot(final, aes(x = zipcode,y = price, fill = neighbourhood_group_cleansed)) + geom_boxplot() + scale_y_continuous(limits = quantile(final$price, c(0, 0.99))) + labs(x = "Zipcode", y = "Price/Night") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + guides(fill = guide_legend(title = "Neighbourhood")) 
ggplotly(pricebyZipcode)
## Warning: Removed 16 rows containing non-finite values (stat_boxplot).

Pair Wise Correlation: Correlation gives us a priliminary idea of how are variables are correlated and which of them could cause multicollinearity

If a property is generously available for booking in the 30 days, it is most likely to be generously available during the rest of the year as well. There is strong positive correlation between (Availablity 30, Availablity 60,Availablity 90 & Availablity 365). The opposite is true. Booked out apartments tend to remain popular across the year.

Interestingly, Number of reviews also drive earlier bookings, which is clearly seen by its positive correlation with column: Availablity 365.

Occupancy - Zipcode Vs Availability Lower the availablity , higher the occupancy - indicating fast-filling properties.

Looking at Availablity for next 365 days, it is surprising to find a lot of properties booked ahead of time. The company can capitalize on this behavior and vary the price accordindingly to generate more revenue/profits.

Zipcodes that make top 10 from this list: 10021,10023,10025,10028,10128,10304,10312,11201,11215,11231

corCols <- c("availability_30","availability_60","availability_90","availability_365","number_of_reviews","price","cost")
plot_correlation(final[corCols])

final %>% group_by(neighbourhood_group_cleansed,zipcode) %>% summarise_all(funs(mean)) %>% 
  ggplot(aes(x =zipcode,y= availability_365, fill = neighbourhood_group_cleansed )) + geom_bar(stat = "identity") + scale_y_continuous(labels = scales::comma) +  scale_colour_brewer(palette = "Pastel2") + labs( y = "Availability", x = "Zipcode") + theme_bw() + theme(plot.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.border = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1)) + guides(fill = guide_legend(title = "Neighbourhood")) 
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

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## Warning in mean.default(neighbourhood): argument is not numeric or logical:
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## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

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## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

Availability Vs Price Stepping aside to understand availablity in terms of price, lower price dont exactly drive faster bookings which is a very important observation while strategizing in investing on real estate. Higher rental properties have minimal availablity (compared to next 365 days). It is satisfying to know that people are ready to pay higher ahead of time which showcases the purchasing power of a customer.

availablityPrice <- final %>% group_by(neighbourhood_group_cleansed,zipcode) %>% summarise_all(funs(mean)) %>% ggplot(aes(x=availability_365, y=price)) + scale_colour_brewer(palette = "Set1") + geom_point(aes(col=neighbourhood_group_cleansed, size=price)) +
  labs(x="Availability", y="Price", shape="Price", colour= "Neighbourhood")
## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_scraped): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA

## Warning in mean.default(neighbourhood): argument is not numeric or logical:
## returning NA
## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA

## Warning in mean.default(neighbourhood_cleansed): argument is not numeric or
## logical: returning NA
## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(property_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA

## Warning in mean.default(room_type): argument is not numeric or logical:
## returning NA
## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA

## Warning in mean.default(security_deposit): argument is not numeric or
## logical: returning NA
## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA

## Warning in mean.default(last_review): argument is not numeric or logical:
## returning NA
## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA

## Warning in mean.default(CountyName): argument is not numeric or logical:
## returning NA
ggplotly(availablityPrice)

Review scores rating Vs Price Are reviews driving the price ? Not likely. Trends are all over the place. Majority of the properties in are highly rated (10). Even if we see a higher rating , we still can see that it doesnt mean the place is very expensive to live or is in a posch locality. Sometimes it is also observed that normal localities also have high rating due to very good customers experience with the hosts.

final$rating <- round(final$review_scores_rating/10,0)
final$rating <- as.factor(final$rating )

reviewPrice <- ggplot(data = subset(final, !is.na(rating)), aes(x=rating, y=price, colour = zipcode)) + geom_point(aes( size=price)) + scale_y_continuous(limits = quantile(final$price, c(0, 0.99))) + labs(x="Rating", y="Price")

ggplotly(reviewPrice)

Performing steps in order to fit various models in order to predict price. Following are the steps done to clean the data again and select the columns which are relevant for model selection and evaluation. We can use various models like xgboost and evaluate the models using various Information criteria like AIC , BIC , SBC etc. We also can plot the ROC curve to understand how well the model is.

inp_data <- read.csv("C:/Users/Kartik/Airbnb/airbnbbbbbbb.csv", header = TRUE,stringsAsFactors = F)
dim(inp_data)
## [1] 48895   106
names(inp_data)
##   [1] "id"                                          
##   [2] "listing_url"                                 
##   [3] "scrape_id"                                   
##   [4] "last_scraped"                                
##   [5] "name"                                        
##   [6] "summary"                                     
##   [7] "space"                                       
##   [8] "description"                                 
##   [9] "experiences_offered"                         
##  [10] "neighborhood_overview"                       
##  [11] "notes"                                       
##  [12] "transit"                                     
##  [13] "access"                                      
##  [14] "interaction"                                 
##  [15] "house_rules"                                 
##  [16] "thumbnail_url"                               
##  [17] "medium_url"                                  
##  [18] "picture_url"                                 
##  [19] "xl_picture_url"                              
##  [20] "host_id"                                     
##  [21] "host_url"                                    
##  [22] "host_name"                                   
##  [23] "host_since"                                  
##  [24] "host_location"                               
##  [25] "host_about"                                  
##  [26] "host_response_time"                          
##  [27] "host_response_rate"                          
##  [28] "host_acceptance_rate"                        
##  [29] "host_is_superhost"                           
##  [30] "host_thumbnail_url"                          
##  [31] "host_picture_url"                            
##  [32] "host_neighbourhood"                          
##  [33] "host_listings_count"                         
##  [34] "host_total_listings_count"                   
##  [35] "host_verifications"                          
##  [36] "host_has_profile_pic"                        
##  [37] "host_identity_verified"                      
##  [38] "street"                                      
##  [39] "neighbourhood"                               
##  [40] "neighbourhood_cleansed"                      
##  [41] "neighbourhood_group_cleansed"                
##  [42] "city"                                        
##  [43] "state"                                       
##  [44] "zipcode"                                     
##  [45] "market"                                      
##  [46] "smart_location"                              
##  [47] "country_code"                                
##  [48] "country"                                     
##  [49] "latitude"                                    
##  [50] "longitude"                                   
##  [51] "is_location_exact"                           
##  [52] "property_type"                               
##  [53] "room_type"                                   
##  [54] "accommodates"                                
##  [55] "bathrooms"                                   
##  [56] "bedrooms"                                    
##  [57] "beds"                                        
##  [58] "bed_type"                                    
##  [59] "amenities"                                   
##  [60] "square_feet"                                 
##  [61] "price"                                       
##  [62] "weekly_price"                                
##  [63] "monthly_price"                               
##  [64] "security_deposit"                            
##  [65] "cleaning_fee"                                
##  [66] "guests_included"                             
##  [67] "extra_people"                                
##  [68] "minimum_nights"                              
##  [69] "maximum_nights"                              
##  [70] "minimum_minimum_nights"                      
##  [71] "maximum_minimum_nights"                      
##  [72] "minimum_maximum_nights"                      
##  [73] "maximum_maximum_nights"                      
##  [74] "minimum_nights_avg_ntm"                      
##  [75] "maximum_nights_avg_ntm"                      
##  [76] "calendar_updated"                            
##  [77] "has_availability"                            
##  [78] "availability_30"                             
##  [79] "availability_60"                             
##  [80] "availability_90"                             
##  [81] "availability_365"                            
##  [82] "calendar_last_scraped"                       
##  [83] "number_of_reviews"                           
##  [84] "number_of_reviews_ltm"                       
##  [85] "first_review"                                
##  [86] "last_review"                                 
##  [87] "review_scores_rating"                        
##  [88] "review_scores_accuracy"                      
##  [89] "review_scores_cleanliness"                   
##  [90] "review_scores_checkin"                       
##  [91] "review_scores_communication"                 
##  [92] "review_scores_location"                      
##  [93] "review_scores_value"                         
##  [94] "requires_license"                            
##  [95] "license"                                     
##  [96] "jurisdiction_names"                          
##  [97] "instant_bookable"                            
##  [98] "is_business_travel_ready"                    
##  [99] "cancellation_policy"                         
## [100] "require_guest_profile_picture"               
## [101] "require_guest_phone_verification"            
## [102] "calculated_host_listings_count"              
## [103] "calculated_host_listings_count_entire_homes" 
## [104] "calculated_host_listings_count_private_rooms"
## [105] "calculated_host_listings_count_shared_rooms" 
## [106] "reviews_per_month"
a <- strsplit(inp_data$amenities,",")

Removing variables that contain only a single value

data1 <- inp_data
a <- apply(data1,2,unique)
is.vector(a)
## [1] TRUE
length(a)
## [1] 106
b <- NULL
for(i in 1:length(a))
{
  if(length(a[[i]])==1){
    b <- rbind(b,names(a[[i]]))
  }
}
b <- as.vector(b)
head(b)
## NULL
data1[,b] <- NULL
dim(data1)
## [1] 48895   106

All the url related variables can be removed as they contain weblinks which aren’t useful for any kind of analysis

data1[,names(data1)[grep("url",names(data1))]] <- NULL
grep("url",names(data1))
## integer(0)

This leads to removal of 8 such variables.Next we removed all those variables which have more than 80% missing values to get more reformed and clean data

a <- apply(data1,2,is.na)
head(a)
##         id scrape_id last_scraped  name summary space description
## [1,] FALSE     FALSE        FALSE FALSE   FALSE FALSE       FALSE
## [2,] FALSE     FALSE        FALSE FALSE   FALSE FALSE       FALSE
## [3,] FALSE     FALSE        FALSE FALSE   FALSE FALSE       FALSE
## [4,] FALSE     FALSE        FALSE FALSE   FALSE FALSE       FALSE
## [5,] FALSE     FALSE        FALSE FALSE   FALSE FALSE       FALSE
## [6,] FALSE     FALSE        FALSE FALSE   FALSE FALSE       FALSE
##      experiences_offered neighborhood_overview notes transit access
## [1,]               FALSE                 FALSE FALSE   FALSE  FALSE
## [2,]               FALSE                 FALSE FALSE   FALSE  FALSE
## [3,]               FALSE                 FALSE FALSE   FALSE  FALSE
## [4,]               FALSE                 FALSE FALSE   FALSE  FALSE
## [5,]               FALSE                 FALSE FALSE   FALSE  FALSE
## [6,]               FALSE                 FALSE FALSE   FALSE  FALSE
##      interaction house_rules host_id host_name host_since host_location
## [1,]       FALSE       FALSE   FALSE     FALSE      FALSE         FALSE
## [2,]       FALSE       FALSE   FALSE     FALSE      FALSE         FALSE
## [3,]       FALSE       FALSE   FALSE     FALSE      FALSE         FALSE
## [4,]       FALSE       FALSE   FALSE     FALSE      FALSE         FALSE
## [5,]       FALSE       FALSE   FALSE     FALSE      FALSE         FALSE
## [6,]       FALSE       FALSE   FALSE     FALSE      FALSE         FALSE
##      host_about host_response_time host_response_rate host_acceptance_rate
## [1,]      FALSE              FALSE              FALSE                FALSE
## [2,]      FALSE              FALSE              FALSE                FALSE
## [3,]      FALSE              FALSE              FALSE                FALSE
## [4,]      FALSE              FALSE              FALSE                FALSE
## [5,]      FALSE              FALSE              FALSE                FALSE
## [6,]      FALSE              FALSE              FALSE                FALSE
##      host_is_superhost host_neighbourhood host_listings_count
## [1,]             FALSE              FALSE               FALSE
## [2,]             FALSE              FALSE               FALSE
## [3,]             FALSE              FALSE               FALSE
## [4,]             FALSE              FALSE               FALSE
## [5,]             FALSE              FALSE               FALSE
## [6,]             FALSE              FALSE               FALSE
##      host_total_listings_count host_verifications host_has_profile_pic
## [1,]                     FALSE              FALSE                FALSE
## [2,]                     FALSE              FALSE                FALSE
## [3,]                     FALSE              FALSE                FALSE
## [4,]                     FALSE              FALSE                FALSE
## [5,]                     FALSE              FALSE                FALSE
## [6,]                     FALSE              FALSE                FALSE
##      host_identity_verified street neighbourhood neighbourhood_cleansed
## [1,]                  FALSE  FALSE         FALSE                  FALSE
## [2,]                  FALSE  FALSE         FALSE                  FALSE
## [3,]                  FALSE  FALSE         FALSE                  FALSE
## [4,]                  FALSE  FALSE         FALSE                  FALSE
## [5,]                  FALSE  FALSE         FALSE                  FALSE
## [6,]                  FALSE  FALSE         FALSE                  FALSE
##      neighbourhood_group_cleansed  city state zipcode market
## [1,]                        FALSE FALSE FALSE   FALSE  FALSE
## [2,]                        FALSE FALSE FALSE   FALSE  FALSE
## [3,]                        FALSE FALSE FALSE   FALSE  FALSE
## [4,]                        FALSE FALSE FALSE   FALSE  FALSE
## [5,]                        FALSE FALSE FALSE   FALSE  FALSE
## [6,]                        FALSE FALSE FALSE   FALSE  FALSE
##      smart_location country_code country latitude longitude
## [1,]          FALSE        FALSE   FALSE    FALSE     FALSE
## [2,]          FALSE        FALSE   FALSE    FALSE     FALSE
## [3,]          FALSE        FALSE   FALSE    FALSE     FALSE
## [4,]          FALSE        FALSE   FALSE    FALSE     FALSE
## [5,]          FALSE        FALSE   FALSE    FALSE     FALSE
## [6,]          FALSE        FALSE   FALSE    FALSE     FALSE
##      is_location_exact property_type room_type accommodates bathrooms
## [1,]             FALSE         FALSE     FALSE        FALSE     FALSE
## [2,]             FALSE         FALSE     FALSE        FALSE     FALSE
## [3,]             FALSE         FALSE     FALSE        FALSE     FALSE
## [4,]             FALSE         FALSE     FALSE        FALSE     FALSE
## [5,]             FALSE         FALSE     FALSE        FALSE     FALSE
## [6,]             FALSE         FALSE     FALSE        FALSE     FALSE
##      bedrooms  beds bed_type amenities square_feet price weekly_price
## [1,]    FALSE FALSE    FALSE     FALSE        TRUE FALSE        FALSE
## [2,]    FALSE FALSE    FALSE     FALSE        TRUE FALSE        FALSE
## [3,]    FALSE FALSE    FALSE     FALSE        TRUE FALSE        FALSE
## [4,]    FALSE FALSE    FALSE     FALSE       FALSE FALSE        FALSE
## [5,]     TRUE FALSE    FALSE     FALSE        TRUE FALSE        FALSE
## [6,]    FALSE FALSE    FALSE     FALSE        TRUE FALSE        FALSE
##      monthly_price security_deposit cleaning_fee guests_included
## [1,]         FALSE            FALSE        FALSE           FALSE
## [2,]         FALSE            FALSE        FALSE           FALSE
## [3,]         FALSE            FALSE        FALSE           FALSE
## [4,]         FALSE            FALSE        FALSE           FALSE
## [5,]         FALSE            FALSE        FALSE           FALSE
## [6,]         FALSE            FALSE        FALSE           FALSE
##      extra_people minimum_nights maximum_nights minimum_minimum_nights
## [1,]        FALSE          FALSE          FALSE                  FALSE
## [2,]        FALSE          FALSE          FALSE                  FALSE
## [3,]        FALSE          FALSE          FALSE                  FALSE
## [4,]        FALSE          FALSE          FALSE                  FALSE
## [5,]        FALSE          FALSE          FALSE                  FALSE
## [6,]        FALSE          FALSE          FALSE                  FALSE
##      maximum_minimum_nights minimum_maximum_nights maximum_maximum_nights
## [1,]                  FALSE                  FALSE                  FALSE
## [2,]                  FALSE                  FALSE                  FALSE
## [3,]                  FALSE                  FALSE                  FALSE
## [4,]                  FALSE                  FALSE                  FALSE
## [5,]                  FALSE                  FALSE                  FALSE
## [6,]                  FALSE                  FALSE                  FALSE
##      minimum_nights_avg_ntm maximum_nights_avg_ntm calendar_updated
## [1,]                  FALSE                  FALSE            FALSE
## [2,]                  FALSE                  FALSE            FALSE
## [3,]                  FALSE                  FALSE            FALSE
## [4,]                  FALSE                  FALSE            FALSE
## [5,]                  FALSE                  FALSE            FALSE
## [6,]                  FALSE                  FALSE            FALSE
##      has_availability availability_30 availability_60 availability_90
## [1,]            FALSE           FALSE           FALSE           FALSE
## [2,]            FALSE           FALSE           FALSE           FALSE
## [3,]            FALSE           FALSE           FALSE           FALSE
## [4,]            FALSE           FALSE           FALSE           FALSE
## [5,]            FALSE           FALSE           FALSE           FALSE
## [6,]            FALSE           FALSE           FALSE           FALSE
##      availability_365 calendar_last_scraped number_of_reviews
## [1,]            FALSE                 FALSE             FALSE
## [2,]            FALSE                 FALSE             FALSE
## [3,]            FALSE                 FALSE             FALSE
## [4,]            FALSE                 FALSE             FALSE
## [5,]            FALSE                 FALSE             FALSE
## [6,]            FALSE                 FALSE             FALSE
##      number_of_reviews_ltm first_review last_review review_scores_rating
## [1,]                 FALSE        FALSE       FALSE                FALSE
## [2,]                 FALSE        FALSE       FALSE                FALSE
## [3,]                 FALSE        FALSE       FALSE                 TRUE
## [4,]                 FALSE        FALSE       FALSE                FALSE
## [5,]                 FALSE        FALSE       FALSE                FALSE
## [6,]                 FALSE        FALSE       FALSE                FALSE
##      review_scores_accuracy review_scores_cleanliness
## [1,]                  FALSE                     FALSE
## [2,]                  FALSE                     FALSE
## [3,]                   TRUE                      TRUE
## [4,]                  FALSE                     FALSE
## [5,]                  FALSE                     FALSE
## [6,]                  FALSE                     FALSE
##      review_scores_checkin review_scores_communication
## [1,]                 FALSE                       FALSE
## [2,]                 FALSE                       FALSE
## [3,]                  TRUE                        TRUE
## [4,]                 FALSE                       FALSE
## [5,]                 FALSE                       FALSE
## [6,]                 FALSE                       FALSE
##      review_scores_location review_scores_value requires_license license
## [1,]                  FALSE               FALSE            FALSE   FALSE
## [2,]                  FALSE               FALSE            FALSE   FALSE
## [3,]                   TRUE                TRUE            FALSE   FALSE
## [4,]                  FALSE               FALSE            FALSE   FALSE
## [5,]                  FALSE               FALSE            FALSE   FALSE
## [6,]                  FALSE               FALSE            FALSE   FALSE
##      jurisdiction_names instant_bookable is_business_travel_ready
## [1,]              FALSE            FALSE                    FALSE
## [2,]              FALSE            FALSE                    FALSE
## [3,]              FALSE            FALSE                    FALSE
## [4,]              FALSE            FALSE                    FALSE
## [5,]              FALSE            FALSE                    FALSE
## [6,]              FALSE            FALSE                    FALSE
##      cancellation_policy require_guest_profile_picture
## [1,]               FALSE                         FALSE
## [2,]               FALSE                         FALSE
## [3,]               FALSE                         FALSE
## [4,]               FALSE                         FALSE
## [5,]               FALSE                         FALSE
## [6,]               FALSE                         FALSE
##      require_guest_phone_verification calculated_host_listings_count
## [1,]                            FALSE                          FALSE
## [2,]                            FALSE                          FALSE
## [3,]                            FALSE                          FALSE
## [4,]                            FALSE                          FALSE
## [5,]                            FALSE                          FALSE
## [6,]                            FALSE                          FALSE
##      calculated_host_listings_count_entire_homes
## [1,]                                       FALSE
## [2,]                                       FALSE
## [3,]                                       FALSE
## [4,]                                       FALSE
## [5,]                                       FALSE
## [6,]                                       FALSE
##      calculated_host_listings_count_private_rooms
## [1,]                                        FALSE
## [2,]                                        FALSE
## [3,]                                        FALSE
## [4,]                                        FALSE
## [5,]                                        FALSE
## [6,]                                        FALSE
##      calculated_host_listings_count_shared_rooms reviews_per_month
## [1,]                                       FALSE             FALSE
## [2,]                                       FALSE             FALSE
## [3,]                                       FALSE              TRUE
## [4,]                                       FALSE             FALSE
## [5,]                                       FALSE             FALSE
## [6,]                                       FALSE             FALSE
b <- apply(a,2,sum)
is.vector(b)  #TRUE
## [1] TRUE
is.list(b)
## [1] FALSE
head(b)
##           id    scrape_id last_scraped         name      summary 
##            0            0            0            0            0 
##        space 
##            0
b/5207>=0.80   #b/5207>=0.80 - missing
##                                           id 
##                                        FALSE 
##                                    scrape_id 
##                                        FALSE 
##                                 last_scraped 
##                                        FALSE 
##                                         name 
##                                        FALSE 
##                                      summary 
##                                        FALSE 
##                                        space 
##                                        FALSE 
##                                  description 
##                                        FALSE 
##                          experiences_offered 
##                                        FALSE 
##                        neighborhood_overview 
##                                        FALSE 
##                                        notes 
##                                        FALSE 
##                                      transit 
##                                        FALSE 
##                                       access 
##                                        FALSE 
##                                  interaction 
##                                        FALSE 
##                                  house_rules 
##                                        FALSE 
##                                      host_id 
##                                        FALSE 
##                                    host_name 
##                                        FALSE 
##                                   host_since 
##                                        FALSE 
##                                host_location 
##                                        FALSE 
##                                   host_about 
##                                        FALSE 
##                           host_response_time 
##                                        FALSE 
##                           host_response_rate 
##                                        FALSE 
##                         host_acceptance_rate 
##                                        FALSE 
##                            host_is_superhost 
##                                        FALSE 
##                           host_neighbourhood 
##                                        FALSE 
##                          host_listings_count 
##                                        FALSE 
##                    host_total_listings_count 
##                                        FALSE 
##                           host_verifications 
##                                        FALSE 
##                         host_has_profile_pic 
##                                        FALSE 
##                       host_identity_verified 
##                                        FALSE 
##                                       street 
##                                        FALSE 
##                                neighbourhood 
##                                        FALSE 
##                       neighbourhood_cleansed 
##                                        FALSE 
##                 neighbourhood_group_cleansed 
##                                        FALSE 
##                                         city 
##                                        FALSE 
##                                        state 
##                                        FALSE 
##                                      zipcode 
##                                        FALSE 
##                                       market 
##                                        FALSE 
##                               smart_location 
##                                        FALSE 
##                                 country_code 
##                                        FALSE 
##                                      country 
##                                        FALSE 
##                                     latitude 
##                                        FALSE 
##                                    longitude 
##                                        FALSE 
##                            is_location_exact 
##                                        FALSE 
##                                property_type 
##                                        FALSE 
##                                    room_type 
##                                        FALSE 
##                                 accommodates 
##                                        FALSE 
##                                    bathrooms 
##                                        FALSE 
##                                     bedrooms 
##                                        FALSE 
##                                         beds 
##                                        FALSE 
##                                     bed_type 
##                                        FALSE 
##                                    amenities 
##                                        FALSE 
##                                  square_feet 
##                                         TRUE 
##                                        price 
##                                        FALSE 
##                                 weekly_price 
##                                        FALSE 
##                                monthly_price 
##                                        FALSE 
##                             security_deposit 
##                                        FALSE 
##                                 cleaning_fee 
##                                        FALSE 
##                              guests_included 
##                                        FALSE 
##                                 extra_people 
##                                        FALSE 
##                               minimum_nights 
##                                        FALSE 
##                               maximum_nights 
##                                        FALSE 
##                       minimum_minimum_nights 
##                                        FALSE 
##                       maximum_minimum_nights 
##                                        FALSE 
##                       minimum_maximum_nights 
##                                        FALSE 
##                       maximum_maximum_nights 
##                                        FALSE 
##                       minimum_nights_avg_ntm 
##                                        FALSE 
##                       maximum_nights_avg_ntm 
##                                        FALSE 
##                             calendar_updated 
##                                        FALSE 
##                             has_availability 
##                                        FALSE 
##                              availability_30 
##                                        FALSE 
##                              availability_60 
##                                        FALSE 
##                              availability_90 
##                                        FALSE 
##                             availability_365 
##                                        FALSE 
##                        calendar_last_scraped 
##                                        FALSE 
##                            number_of_reviews 
##                                        FALSE 
##                        number_of_reviews_ltm 
##                                        FALSE 
##                                 first_review 
##                                        FALSE 
##                                  last_review 
##                                        FALSE 
##                         review_scores_rating 
##                                         TRUE 
##                       review_scores_accuracy 
##                                         TRUE 
##                    review_scores_cleanliness 
##                                         TRUE 
##                        review_scores_checkin 
##                                         TRUE 
##                  review_scores_communication 
##                                         TRUE 
##                       review_scores_location 
##                                         TRUE 
##                          review_scores_value 
##                                         TRUE 
##                             requires_license 
##                                        FALSE 
##                                      license 
##                                        FALSE 
##                           jurisdiction_names 
##                                        FALSE 
##                             instant_bookable 
##                                        FALSE 
##                     is_business_travel_ready 
##                                        FALSE 
##                          cancellation_policy 
##                                        FALSE 
##                require_guest_profile_picture 
##                                        FALSE 
##             require_guest_phone_verification 
##                                        FALSE 
##               calculated_host_listings_count 
##                                        FALSE 
##  calculated_host_listings_count_entire_homes 
##                                        FALSE 
## calculated_host_listings_count_private_rooms 
##                                        FALSE 
##  calculated_host_listings_count_shared_rooms 
##                                        FALSE 
##                            reviews_per_month 
##                                         TRUE
b[b/5207>=0.80] 
##                 square_feet        review_scores_rating 
##                       48487                       11022 
##      review_scores_accuracy   review_scores_cleanliness 
##                       11060                       11043 
##       review_scores_checkin review_scores_communication 
##                       11078                       11055 
##      review_scores_location         review_scores_value 
##                       11082                       11080 
##           reviews_per_month 
##                       10052
names(b[b/5207>=0.80])   
## [1] "square_feet"                 "review_scores_rating"       
## [3] "review_scores_accuracy"      "review_scores_cleanliness"  
## [5] "review_scores_checkin"       "review_scores_communication"
## [7] "review_scores_location"      "review_scores_value"        
## [9] "reviews_per_month"
data1[,names(b[b/5207>=0.80])] <- NULL
head(data1$square_feet)
## NULL

This leads to a removal of variable Based on intuition after analyzing the data and further inspection we observed that below variables does not add any value to our analysis and should be removed. Also having performed EDA on the same dataset we have a more clear view of which variables contribute to predicting our target variable price

 data1[,c("security_deposit","weekly_price","monthly_price","first_review","last_review","jurisdiction_names","zipcode","street","market","cleaning_fee","name","interaction","access","space","notes","description","host_name","host_has_profile_pic","host_verifications","host_neighborhood","require_guest_profile_picture","require_guest_phone_verification","calculated_host_listings_count", "host_location","transit","neighborhood_overview","house_rules","host_about","license", "requires_license","host_neighbourhood")] <- NULL

Removing variables with duplicated values

data1[,names(data1[(duplicated(t(data1)))])] <- NULL

We observed that different listings have different amenities and hence we created flag/dummy variables for the most important amenities we thought a user looks for while booking at Airbnb. These amenities are TV, Internet, Air Conditioning, Breakfast, Kitchen, and Pets. Adding amenities categories

a <- data1$amenities
data1$TV <- ifelse(grepl("TV",a, ignore.case = T)==T,1,0)
data1$Internet <- ifelse(grepl("Internet",a, ignore.case= T)==T,1,0)
data1$AirCondition <- ifelse(grepl("conditioning",a, ignore.case =T)==T,1,0)
data1$Pets <- ifelse(grepl("Pet",a, ignore.case = T)==T,1,0)
data1$Pets <- ifelse(grepl("Dog",a, ignore.case = T)==T,1,data1$Pets)
data1$Pets <- ifelse(grepl("Cat",a, ignore.case = T)==T,1,data1$Pets)
data1$Kitchen <- ifelse(grepl("Kitchen",a, ignore.case = T)==T,1,0)
data1$Breakfast <- ifelse(grepl("breakfast",a, ignore.case = T)==T,1,0)
data1[,c("amenities")] <- NULL

The data types of variables which we thought might be significant were converted to the appropriate data types. Converting price variable to integer

data1$price <- sub("\\$","",data1$price)
data1$price <- sub(",","",data1$price)
data1$price <- as.integer(data1$price)
data1$host_response_time <- as.factor(data1$host_response_time)
data1$host_is_superhost <- as.factor(data1$host_is_superhost)
data1$host_identity_verified <- as.factor(data1$host_identity_verified)
data1$neighbourhood_cleansed <- as.factor(data1$neighbourhood_cleansed)
data1$is_location_exact <- as.factor(data1$is_location_exact)
data1$property_type <- as.factor(data1$property_type)
data1$room_type <- as.factor(data1$room_type)
data1$bed_type <- as.factor(data1$bed_type)
data1$calendar_updated <- as.factor(data1$calendar_updated)
data1$instant_bookable <- as.factor(data1$instant_bookable)
data1$cancellation_policy <- as.factor(data1$cancellation_policy)

Treating host_response_rate and extra_people from character to a numeric variable for modeling purpose using SWOE method

data1$host_response_rate<- as.numeric(sub("%", "", data1$host_response_rate))
## Warning: NAs introduced by coercion
data1$host_response_rate <- data1$host_response_rate/100
data1$extra_people <- as.numeric(sub("\\$","",data1$extra_people))

Removing insignificant listings which have price = 0

data1 <- data1[-c(717,1334,3093),]

We also created a variable about the number of days since the listing was on Airbnb

data1$host_since <- as.Date(data1$host_since)
data1$host_since <- as.Date("2017-05-10")-data1$host_since

In order to reduce the number of levels in factor variables with large number of levels, we clubbed the factors with low number of listings. In the dataset, we observed that neighbourhood_cleansed, which gives us an idea of the nighbourhood of the listing, has 72 levels.

a <- data1 %>% group_by(neighbourhood_cleansed) %>% summarise(len = length(neighbourhood_cleansed))

data1 <- merge(data1,a,by = "neighbourhood_cleansed")
data1$neighbourhood_cleansed <- as.character(data1$neighbourhood_cleansed)
data1$neighbourhood_cleansed <- ifelse(data1$len<150,"Others",data1$neighbourhood_cleansed)
data1$len <- NULL

Missing Value Treatment checking missing values in different columns

missing = data.frame(col=colnames(data1), type = sapply(data1, class), 
missing = sapply(data1, function(x) sum(is.na(x)| x=="" | x=="N/A"))/nrow(data1) * 100)

Replace missing values in host_response_time with the most common occurring category (its mode)

data1$host_response_time<- sub("N/A","within an hour", data1$host_response_time)

Replace missing values in host_response rate with mean values observed for host_response_time when it is within an hour

data1$host_response_rate <- ifelse(is.na(data1$host_response_rate)==T,0.99,data1$host_response_rate)

Replace missing values in bathrooms, bedrooms and beds with the median value

data1$bathrooms <- ifelse(is.na(data1$bathrooms)==T, 1, data1$bathrooms)
data1$bedrooms <- ifelse(is.na(data1$bedrooms)==T,1,data1$bedrooms)
data1$beds <- ifelse(is.na(data1$beds)==T,1,data1$beds)

Outliers Histogram of price

ggplot(data=data1, aes(price)) + 
geom_histogram(fill="red") + 
labs(title="Histogram of Price") +
labs(x="Price", y="Count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Percentile of price

quantile(data1$price, c(.9, .95, .97, 0.975, 0.98, 0.99, 0.995, 0.999, 0.9999))
##    90%    95%    97%  97.5%    98%    99%  99.5%  99.9% 99.99% 
##    269    355    450    500    550    799   1000   3000   9999

As we can see from the histogram as well as the percentile distribution of Price, there are extreme values in Price,So we performed Winsorization at 99% level and captured the maximum value of price at 650 USD. Capture the extreme values of Price at 99 percentile level

data1$price <- ifelse(data1$price>650,650,data1$price)
ggplot(data=data1, aes(price)) + 
  geom_histogram(fill="red") + 
  labs(title="Histogram of Price") +
  labs(x="Price", y="Count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Visualizing the distribution of no of reviews for different cancellation policy using a boxplot

ggboxplot(inp_data, x = "cancellation_policy", y = "number_of_reviews", 
          color = "cancellation_policy",
          ylab = "Number of reviews", xlab = "Cancellation Policy")

Initial inspection of the data using the boxplot suggests that there are differences in the booking rates for different cancellation policies: the accommodations with Strict and Moderate cancellation policies have higher average booking rates (staying rate). To investigate for if there are significant differences among groups quantitatively, we then fited an one-way ANOVA model as below.

Compute the analysis of variance

anova1 <- aov(number_of_reviews ~ cancellation_policy, data = data1)

Summary of the analysis

summary(anova1)
##                        Df   Sum Sq Mean Sq F value Pr(>F)    
## cancellation_policy     5  4618644  923729   488.7 <2e-16 ***
## Residuals           48886 92408010    1890                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Since, p-value is less than the significance level 0.05 in the moel summary,we reject the null hypothesis and we can say that the number of reviews has an impact on cancellation policies.

names(data1)
##  [1] "neighbourhood_cleansed"                      
##  [2] "id"                                          
##  [3] "scrape_id"                                   
##  [4] "last_scraped"                                
##  [5] "summary"                                     
##  [6] "experiences_offered"                         
##  [7] "host_id"                                     
##  [8] "host_since"                                  
##  [9] "host_response_time"                          
## [10] "host_response_rate"                          
## [11] "host_acceptance_rate"                        
## [12] "host_is_superhost"                           
## [13] "host_listings_count"                         
## [14] "host_identity_verified"                      
## [15] "neighbourhood"                               
## [16] "neighbourhood_group_cleansed"                
## [17] "city"                                        
## [18] "state"                                       
## [19] "smart_location"                              
## [20] "country_code"                                
## [21] "country"                                     
## [22] "latitude"                                    
## [23] "longitude"                                   
## [24] "is_location_exact"                           
## [25] "property_type"                               
## [26] "room_type"                                   
## [27] "accommodates"                                
## [28] "bathrooms"                                   
## [29] "bedrooms"                                    
## [30] "beds"                                        
## [31] "bed_type"                                    
## [32] "price"                                       
## [33] "guests_included"                             
## [34] "extra_people"                                
## [35] "minimum_nights"                              
## [36] "maximum_nights"                              
## [37] "minimum_minimum_nights"                      
## [38] "maximum_minimum_nights"                      
## [39] "minimum_maximum_nights"                      
## [40] "maximum_maximum_nights"                      
## [41] "minimum_nights_avg_ntm"                      
## [42] "maximum_nights_avg_ntm"                      
## [43] "calendar_updated"                            
## [44] "has_availability"                            
## [45] "availability_30"                             
## [46] "availability_60"                             
## [47] "availability_90"                             
## [48] "availability_365"                            
## [49] "number_of_reviews"                           
## [50] "number_of_reviews_ltm"                       
## [51] "instant_bookable"                            
## [52] "is_business_travel_ready"                    
## [53] "cancellation_policy"                         
## [54] "calculated_host_listings_count_entire_homes" 
## [55] "calculated_host_listings_count_private_rooms"
## [56] "calculated_host_listings_count_shared_rooms" 
## [57] "TV"                                          
## [58] "Internet"                                    
## [59] "AirCondition"                                
## [60] "Pets"                                        
## [61] "Kitchen"                                     
## [62] "Breakfast"

Visualizing the distribution of review_scores_rating for different bed type using a boxplot

ggboxplot(data1, x = "bed_type", y = "price", 
          color = "bed_type",
          ylab = "price", xlab = "Bed Type")

Initial inspection of the data using the boxplot suggests that there are no differences in the review rating for different bed types. To investigate for if there are differences among different groups quantitatively, we fitted an one-way ANOVA model.

Compute the analysis of variance

anova2 <- aov(price ~ bed_type, data = data1)

Summary of the analysis

summary(anova2)
##                Df    Sum Sq Mean Sq F value Pr(>F)    
## bed_type        4   1209359  302340   23.66 <2e-16 ***
## Residuals   48887 624598530   12776                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Since P-value is smaller than 0.05, we reject the null hypothesis that the price varies based on the bed types.

lm

data_size <- floor(0.8 * nrow(data1))
train_data <- sample(seq_len(nrow(data1)), size = data_size)
train <- data1[train_data, ]
test <- data1[-train_data, ]
names(data1)
##  [1] "neighbourhood_cleansed"                      
##  [2] "id"                                          
##  [3] "scrape_id"                                   
##  [4] "last_scraped"                                
##  [5] "summary"                                     
##  [6] "experiences_offered"                         
##  [7] "host_id"                                     
##  [8] "host_since"                                  
##  [9] "host_response_time"                          
## [10] "host_response_rate"                          
## [11] "host_acceptance_rate"                        
## [12] "host_is_superhost"                           
## [13] "host_listings_count"                         
## [14] "host_identity_verified"                      
## [15] "neighbourhood"                               
## [16] "neighbourhood_group_cleansed"                
## [17] "city"                                        
## [18] "state"                                       
## [19] "smart_location"                              
## [20] "country_code"                                
## [21] "country"                                     
## [22] "latitude"                                    
## [23] "longitude"                                   
## [24] "is_location_exact"                           
## [25] "property_type"                               
## [26] "room_type"                                   
## [27] "accommodates"                                
## [28] "bathrooms"                                   
## [29] "bedrooms"                                    
## [30] "beds"                                        
## [31] "bed_type"                                    
## [32] "price"                                       
## [33] "guests_included"                             
## [34] "extra_people"                                
## [35] "minimum_nights"                              
## [36] "maximum_nights"                              
## [37] "minimum_minimum_nights"                      
## [38] "maximum_minimum_nights"                      
## [39] "minimum_maximum_nights"                      
## [40] "maximum_maximum_nights"                      
## [41] "minimum_nights_avg_ntm"                      
## [42] "maximum_nights_avg_ntm"                      
## [43] "calendar_updated"                            
## [44] "has_availability"                            
## [45] "availability_30"                             
## [46] "availability_60"                             
## [47] "availability_90"                             
## [48] "availability_365"                            
## [49] "number_of_reviews"                           
## [50] "number_of_reviews_ltm"                       
## [51] "instant_bookable"                            
## [52] "is_business_travel_ready"                    
## [53] "cancellation_policy"                         
## [54] "calculated_host_listings_count_entire_homes" 
## [55] "calculated_host_listings_count_private_rooms"
## [56] "calculated_host_listings_count_shared_rooms" 
## [57] "TV"                                          
## [58] "Internet"                                    
## [59] "AirCondition"                                
## [60] "Pets"                                        
## [61] "Kitchen"                                     
## [62] "Breakfast"
price_model <- lm(price ~ as.factor(neighbourhood_cleansed) + host_since + Breakfast + Kitchen + Pets + AirCondition + Internet + TV + number_of_reviews + number_of_reviews_ltm + as.factor(cancellation_policy) + guests_included + beds + as.factor(bed_type) + bathrooms + as.factor(property_type) + as.factor(room_type), 
data = train)
summary(price_model)
## 
## Call:
## lm(formula = price ~ as.factor(neighbourhood_cleansed) + host_since + 
##     Breakfast + Kitchen + Pets + AirCondition + Internet + TV + 
##     number_of_reviews + number_of_reviews_ltm + as.factor(cancellation_policy) + 
##     guests_included + beds + as.factor(bed_type) + bathrooms + 
##     as.factor(property_type) + as.factor(room_type), data = train)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -897.08  -40.69   -8.83   22.35  666.27 
## 
## Coefficients:
##                                                              Estimate
## (Intercept)                                                 1.050e+02
## as.factor(neighbourhood_cleansed)Bedford-Stuyvesant        -1.347e+01
## as.factor(neighbourhood_cleansed)Boerum Hill                2.902e+01
## as.factor(neighbourhood_cleansed)Brooklyn Heights           6.426e+01
## as.factor(neighbourhood_cleansed)Bushwick                  -1.618e+01
## as.factor(neighbourhood_cleansed)Carroll Gardens            2.605e+01
## as.factor(neighbourhood_cleansed)Chelsea                    8.778e+01
## as.factor(neighbourhood_cleansed)Chinatown                  4.485e+01
## as.factor(neighbourhood_cleansed)Clinton Hill               2.259e+01
## as.factor(neighbourhood_cleansed)Crown Heights             -9.491e+00
## as.factor(neighbourhood_cleansed)Ditmars Steinway          -2.376e+00
## as.factor(neighbourhood_cleansed)East Elmhurst             -1.835e+01
## as.factor(neighbourhood_cleansed)East Flatbush             -2.287e+01
## as.factor(neighbourhood_cleansed)East Harlem                1.460e+01
## as.factor(neighbourhood_cleansed)East New York             -2.916e+01
## as.factor(neighbourhood_cleansed)East Village               5.781e+01
## as.factor(neighbourhood_cleansed)Elmhurst                  -1.844e+01
## as.factor(neighbourhood_cleansed)Financial District         7.176e+01
## as.factor(neighbourhood_cleansed)Flatbush                  -1.412e+01
## as.factor(neighbourhood_cleansed)Flushing                  -1.378e+01
## as.factor(neighbourhood_cleansed)Fort Greene                2.498e+01
## as.factor(neighbourhood_cleansed)Gowanus                    1.727e+01
## as.factor(neighbourhood_cleansed)Gramercy                   7.035e+01
## as.factor(neighbourhood_cleansed)Greenpoint                 1.473e+01
## as.factor(neighbourhood_cleansed)Greenwich Village          9.107e+01
## as.factor(neighbourhood_cleansed)Harlem                     7.915e+00
## as.factor(neighbourhood_cleansed)Hell's Kitchen             7.500e+01
## as.factor(neighbourhood_cleansed)Inwood                    -1.292e+01
## as.factor(neighbourhood_cleansed)Jackson Heights           -1.510e+01
## as.factor(neighbourhood_cleansed)Jamaica                   -2.251e+01
## as.factor(neighbourhood_cleansed)Kensington                -1.831e+01
## as.factor(neighbourhood_cleansed)Kips Bay                   5.978e+01
## as.factor(neighbourhood_cleansed)Long Island City           1.430e+01
## as.factor(neighbourhood_cleansed)Lower East Side            5.227e+01
## as.factor(neighbourhood_cleansed)Midtown                    1.084e+02
## as.factor(neighbourhood_cleansed)Morningside Heights        1.285e+01
## as.factor(neighbourhood_cleansed)Murray Hill                6.968e+01
## as.factor(neighbourhood_cleansed)Nolita                     8.684e+01
## as.factor(neighbourhood_cleansed)Others                    -6.102e+00
## as.factor(neighbourhood_cleansed)Park Slope                 2.499e+01
## as.factor(neighbourhood_cleansed)Prospect-Lefferts Gardens -1.661e+01
## as.factor(neighbourhood_cleansed)Prospect Heights           2.434e+01
## as.factor(neighbourhood_cleansed)Ridgewood                 -2.747e+01
## as.factor(neighbourhood_cleansed)Sheepshead Bay            -1.538e+01
## as.factor(neighbourhood_cleansed)SoHo                       1.193e+02
## as.factor(neighbourhood_cleansed)South Slope                9.181e+00
## as.factor(neighbourhood_cleansed)Sunnyside                 -1.244e+01
## as.factor(neighbourhood_cleansed)Sunset Park               -8.644e+00
## as.factor(neighbourhood_cleansed)Theater District           9.229e+01
## as.factor(neighbourhood_cleansed)Tribeca                    1.659e+02
## as.factor(neighbourhood_cleansed)Upper East Side            4.598e+01
## as.factor(neighbourhood_cleansed)Upper West Side            5.519e+01
## as.factor(neighbourhood_cleansed)Washington Heights        -4.261e+00
## as.factor(neighbourhood_cleansed)West Village               1.061e+02
## as.factor(neighbourhood_cleansed)Williamsburg               2.791e+01
## as.factor(neighbourhood_cleansed)Windsor Terrace           -4.090e+00
## as.factor(neighbourhood_cleansed)Woodside                  -1.287e+01
## host_since                                                 -1.627e-03
## Breakfast                                                   1.364e+01
## Kitchen                                                    -9.412e+00
## Pets                                                        6.244e+00
## AirCondition                                                3.324e+00
## Internet                                                    8.808e-01
## TV                                                          1.151e+01
## number_of_reviews                                          -4.102e-02
## number_of_reviews_ltm                                      -3.419e-01
## as.factor(cancellation_policy)moderate                     -9.295e+00
## as.factor(cancellation_policy)strict                       -6.083e+01
## as.factor(cancellation_policy)strict_14_with_grace_period  -4.209e+00
## as.factor(cancellation_policy)super_strict_30              -8.605e+01
## as.factor(cancellation_policy)super_strict_60               4.943e+01
## guests_included                                             1.230e+01
## beds                                                        1.899e+01
## as.factor(bed_type)Couch                                    1.548e+01
## as.factor(bed_type)Futon                                   -1.200e+01
## as.factor(bed_type)Pull-out Sofa                           -1.235e+01
## as.factor(bed_type)Real Bed                                -1.058e+01
## bathrooms                                                   4.725e+01
## as.factor(property_type)Apartment                          -4.504e+01
## as.factor(property_type)Barn                               -2.377e+01
## as.factor(property_type)Bed and breakfast                  -3.605e+01
## as.factor(property_type)Boat                                5.979e+01
## as.factor(property_type)Boutique hotel                      5.532e+01
## as.factor(property_type)Bungalow                           -5.200e+00
## as.factor(property_type)Bus                                -4.859e+00
## as.factor(property_type)Cabin                               2.908e+00
## as.factor(property_type)Camper/RV                          -1.056e+02
## as.factor(property_type)Casa particular (Cuba)             -4.155e+01
## as.factor(property_type)Castle                             -2.996e+01
## as.factor(property_type)Cave                                1.731e+01
## as.factor(property_type)Condominium                        -9.800e+00
## as.factor(property_type)Cottage                            -5.562e+01
## as.factor(property_type)Dome house                         -5.925e+01
## as.factor(property_type)Earth house                        -1.085e+02
## as.factor(property_type)Farm stay                          -4.341e+01
## as.factor(property_type)Guest suite                        -5.898e+01
## as.factor(property_type)Guesthouse                         -3.762e+01
## as.factor(property_type)Hostel                             -9.385e+01
## as.factor(property_type)Hotel                              -2.554e+01
## as.factor(property_type)House                              -4.328e+01
## as.factor(property_type)Houseboat                           1.039e+02
## as.factor(property_type)Loft                                1.396e+00
## as.factor(property_type)Nature lodge                       -5.047e+01
## as.factor(property_type)Other                              -1.949e+01
## as.factor(property_type)Resort                              1.730e+02
## as.factor(property_type)Serviced apartment                 -2.445e+01
## as.factor(property_type)Tent                                3.713e+01
## as.factor(property_type)Tiny house                         -5.363e+01
## as.factor(property_type)Townhouse                          -3.397e+01
## as.factor(property_type)Villa                              -2.875e+00
## as.factor(property_type)Yurt                               -7.172e+01
## as.factor(room_type)Private room                           -6.588e+01
## as.factor(room_type)Shared room                            -1.001e+02
##                                                            Std. Error
## (Intercept)                                                 2.539e+01
## as.factor(neighbourhood_cleansed)Bedford-Stuyvesant         3.317e+00
## as.factor(neighbourhood_cleansed)Boerum Hill                7.306e+00
## as.factor(neighbourhood_cleansed)Brooklyn Heights           7.677e+00
## as.factor(neighbourhood_cleansed)Bushwick                   3.476e+00
## as.factor(neighbourhood_cleansed)Carroll Gardens            6.529e+00
## as.factor(neighbourhood_cleansed)Chelsea                    4.028e+00
## as.factor(neighbourhood_cleansed)Chinatown                  5.555e+00
## as.factor(neighbourhood_cleansed)Clinton Hill               4.798e+00
## as.factor(neighbourhood_cleansed)Crown Heights              3.742e+00
## as.factor(neighbourhood_cleansed)Ditmars Steinway           5.949e+00
## as.factor(neighbourhood_cleansed)East Elmhurst              7.355e+00
## as.factor(neighbourhood_cleansed)East Flatbush              5.045e+00
## as.factor(neighbourhood_cleansed)East Harlem                4.001e+00
## as.factor(neighbourhood_cleansed)East New York              6.848e+00
## as.factor(neighbourhood_cleansed)East Village               3.637e+00
## as.factor(neighbourhood_cleansed)Elmhurst                   6.487e+00
## as.factor(neighbourhood_cleansed)Financial District         4.415e+00
## as.factor(neighbourhood_cleansed)Flatbush                   4.581e+00
## as.factor(neighbourhood_cleansed)Flushing                   5.283e+00
## as.factor(neighbourhood_cleansed)Fort Greene                5.033e+00
## as.factor(neighbourhood_cleansed)Gowanus                    6.338e+00
## as.factor(neighbourhood_cleansed)Gramercy                   5.700e+00
## as.factor(neighbourhood_cleansed)Greenpoint                 3.986e+00
## as.factor(neighbourhood_cleansed)Greenwich Village          5.374e+00
## as.factor(neighbourhood_cleansed)Harlem                     3.438e+00
## as.factor(neighbourhood_cleansed)Hell's Kitchen             3.607e+00
## as.factor(neighbourhood_cleansed)Inwood                     6.355e+00
## as.factor(neighbourhood_cleansed)Jackson Heights            7.317e+00
## as.factor(neighbourhood_cleansed)Jamaica                    6.672e+00
## as.factor(neighbourhood_cleansed)Kensington                 7.322e+00
## as.factor(neighbourhood_cleansed)Kips Bay                   5.114e+00
## as.factor(neighbourhood_cleansed)Long Island City           4.983e+00
## as.factor(neighbourhood_cleansed)Lower East Side            4.189e+00
## as.factor(neighbourhood_cleansed)Midtown                    3.829e+00
## as.factor(neighbourhood_cleansed)Morningside Heights        5.648e+00
## as.factor(neighbourhood_cleansed)Murray Hill                5.005e+00
## as.factor(neighbourhood_cleansed)Nolita                     6.504e+00
## as.factor(neighbourhood_cleansed)Others                     3.231e+00
## as.factor(neighbourhood_cleansed)Park Slope                 4.956e+00
## as.factor(neighbourhood_cleansed)Prospect-Lefferts Gardens  4.863e+00
## as.factor(neighbourhood_cleansed)Prospect Heights           5.655e+00
## as.factor(neighbourhood_cleansed)Ridgewood                  5.272e+00
## as.factor(neighbourhood_cleansed)Sheepshead Bay             7.495e+00
## as.factor(neighbourhood_cleansed)SoHo                       5.638e+00
## as.factor(neighbourhood_cleansed)South Slope                5.998e+00
## as.factor(neighbourhood_cleansed)Sunnyside                  5.535e+00
## as.factor(neighbourhood_cleansed)Sunset Park                5.363e+00
## as.factor(neighbourhood_cleansed)Theater District           6.146e+00
## as.factor(neighbourhood_cleansed)Tribeca                    7.544e+00
## as.factor(neighbourhood_cleansed)Upper East Side            3.664e+00
## as.factor(neighbourhood_cleansed)Upper West Side            3.603e+00
## as.factor(neighbourhood_cleansed)Washington Heights         4.192e+00
## as.factor(neighbourhood_cleansed)West Village               4.418e+00
## as.factor(neighbourhood_cleansed)Williamsburg               3.306e+00
## as.factor(neighbourhood_cleansed)Windsor Terrace            7.677e+00
## as.factor(neighbourhood_cleansed)Woodside                   6.438e+00
## host_since                                                  5.571e-04
## Breakfast                                                   1.517e+00
## Kitchen                                                     1.569e+00
## Pets                                                        1.065e+00
## AirCondition                                                1.199e+00
## Internet                                                    9.904e-01
## TV                                                          9.349e-01
## number_of_reviews                                           1.572e-02
## number_of_reviews_ltm                                       4.287e-02
## as.factor(cancellation_policy)moderate                      1.145e+00
## as.factor(cancellation_policy)strict                        5.658e+01
## as.factor(cancellation_policy)strict_14_with_grace_period   9.961e-01
## as.factor(cancellation_policy)super_strict_30               1.898e+01
## as.factor(cancellation_policy)super_strict_60               8.445e+00
## guests_included                                             4.173e-01
## beds                                                        4.657e-01
## as.factor(bed_type)Couch                                    1.306e+01
## as.factor(bed_type)Futon                                    8.559e+00
## as.factor(bed_type)Pull-out Sofa                            8.818e+00
## as.factor(bed_type)Real Bed                                 6.850e+00
## bathrooms                                                   1.036e+00
## as.factor(property_type)Apartment                           2.420e+01
## as.factor(property_type)Barn                                8.361e+01
## as.factor(property_type)Bed and breakfast                   2.594e+01
## as.factor(property_type)Boat                                3.605e+01
## as.factor(property_type)Boutique hotel                      2.512e+01
## as.factor(property_type)Bungalow                            2.943e+01
## as.factor(property_type)Bus                                 8.358e+01
## as.factor(property_type)Cabin                               4.681e+01
## as.factor(property_type)Camper/RV                           3.423e+01
## as.factor(property_type)Casa particular (Cuba)              5.219e+01
## as.factor(property_type)Castle                              8.359e+01
## as.factor(property_type)Cave                                6.153e+01
## as.factor(property_type)Condominium                         2.430e+01
## as.factor(property_type)Cottage                             5.215e+01
## as.factor(property_type)Dome house                          6.152e+01
## as.factor(property_type)Earth house                         5.217e+01
## as.factor(property_type)Farm stay                           6.157e+01
## as.factor(property_type)Guest suite                         2.469e+01
## as.factor(property_type)Guesthouse                          2.682e+01
## as.factor(property_type)Hostel                              2.654e+01
## as.factor(property_type)Hotel                               2.489e+01
## as.factor(property_type)House                               2.425e+01
## as.factor(property_type)Houseboat                           5.217e+01
## as.factor(property_type)Loft                                2.432e+01
## as.factor(property_type)Nature lodge                        8.357e+01
## as.factor(property_type)Other                               2.544e+01
## as.factor(property_type)Resort                              2.619e+01
## as.factor(property_type)Serviced apartment                  2.450e+01
## as.factor(property_type)Tent                                4.677e+01
## as.factor(property_type)Tiny house                          3.041e+01
## as.factor(property_type)Townhouse                           2.431e+01
## as.factor(property_type)Villa                               2.995e+01
## as.factor(property_type)Yurt                                6.153e+01
## as.factor(room_type)Private room                            9.792e-01
## as.factor(room_type)Shared room                             2.799e+00
##                                                            t value
## (Intercept)                                                  4.135
## as.factor(neighbourhood_cleansed)Bedford-Stuyvesant         -4.061
## as.factor(neighbourhood_cleansed)Boerum Hill                 3.972
## as.factor(neighbourhood_cleansed)Brooklyn Heights            8.370
## as.factor(neighbourhood_cleansed)Bushwick                   -4.654
## as.factor(neighbourhood_cleansed)Carroll Gardens             3.989
## as.factor(neighbourhood_cleansed)Chelsea                    21.794
## as.factor(neighbourhood_cleansed)Chinatown                   8.075
## as.factor(neighbourhood_cleansed)Clinton Hill                4.708
## as.factor(neighbourhood_cleansed)Crown Heights              -2.536
## as.factor(neighbourhood_cleansed)Ditmars Steinway           -0.399
## as.factor(neighbourhood_cleansed)East Elmhurst              -2.495
## as.factor(neighbourhood_cleansed)East Flatbush              -4.534
## as.factor(neighbourhood_cleansed)East Harlem                 3.648
## as.factor(neighbourhood_cleansed)East New York              -4.258
## as.factor(neighbourhood_cleansed)East Village               15.893
## as.factor(neighbourhood_cleansed)Elmhurst                   -2.843
## as.factor(neighbourhood_cleansed)Financial District         16.253
## as.factor(neighbourhood_cleansed)Flatbush                   -3.081
## as.factor(neighbourhood_cleansed)Flushing                   -2.608
## as.factor(neighbourhood_cleansed)Fort Greene                 4.963
## as.factor(neighbourhood_cleansed)Gowanus                     2.725
## as.factor(neighbourhood_cleansed)Gramercy                   12.341
## as.factor(neighbourhood_cleansed)Greenpoint                  3.696
## as.factor(neighbourhood_cleansed)Greenwich Village          16.945
## as.factor(neighbourhood_cleansed)Harlem                      2.302
## as.factor(neighbourhood_cleansed)Hell's Kitchen             20.791
## as.factor(neighbourhood_cleansed)Inwood                     -2.034
## as.factor(neighbourhood_cleansed)Jackson Heights            -2.064
## as.factor(neighbourhood_cleansed)Jamaica                    -3.374
## as.factor(neighbourhood_cleansed)Kensington                 -2.501
## as.factor(neighbourhood_cleansed)Kips Bay                   11.689
## as.factor(neighbourhood_cleansed)Long Island City            2.869
## as.factor(neighbourhood_cleansed)Lower East Side            12.480
## as.factor(neighbourhood_cleansed)Midtown                    28.321
## as.factor(neighbourhood_cleansed)Morningside Heights         2.275
## as.factor(neighbourhood_cleansed)Murray Hill                13.921
## as.factor(neighbourhood_cleansed)Nolita                     13.353
## as.factor(neighbourhood_cleansed)Others                     -1.889
## as.factor(neighbourhood_cleansed)Park Slope                  5.042
## as.factor(neighbourhood_cleansed)Prospect-Lefferts Gardens  -3.417
## as.factor(neighbourhood_cleansed)Prospect Heights            4.304
## as.factor(neighbourhood_cleansed)Ridgewood                  -5.210
## as.factor(neighbourhood_cleansed)Sheepshead Bay             -2.052
## as.factor(neighbourhood_cleansed)SoHo                       21.164
## as.factor(neighbourhood_cleansed)South Slope                 1.531
## as.factor(neighbourhood_cleansed)Sunnyside                  -2.247
## as.factor(neighbourhood_cleansed)Sunset Park                -1.612
## as.factor(neighbourhood_cleansed)Theater District           15.016
## as.factor(neighbourhood_cleansed)Tribeca                    21.995
## as.factor(neighbourhood_cleansed)Upper East Side            12.550
## as.factor(neighbourhood_cleansed)Upper West Side            15.319
## as.factor(neighbourhood_cleansed)Washington Heights         -1.016
## as.factor(neighbourhood_cleansed)West Village               24.007
## as.factor(neighbourhood_cleansed)Williamsburg                8.444
## as.factor(neighbourhood_cleansed)Windsor Terrace            -0.533
## as.factor(neighbourhood_cleansed)Woodside                   -2.000
## host_since                                                  -2.921
## Breakfast                                                    8.993
## Kitchen                                                     -5.997
## Pets                                                         5.866
## AirCondition                                                 2.771
## Internet                                                     0.889
## TV                                                          12.312
## number_of_reviews                                           -2.609
## number_of_reviews_ltm                                       -7.975
## as.factor(cancellation_policy)moderate                      -8.116
## as.factor(cancellation_policy)strict                        -1.075
## as.factor(cancellation_policy)strict_14_with_grace_period   -4.226
## as.factor(cancellation_policy)super_strict_30               -4.534
## as.factor(cancellation_policy)super_strict_60                5.854
## guests_included                                             29.467
## beds                                                        40.787
## as.factor(bed_type)Couch                                     1.185
## as.factor(bed_type)Futon                                    -1.402
## as.factor(bed_type)Pull-out Sofa                            -1.401
## as.factor(bed_type)Real Bed                                 -1.545
## bathrooms                                                   45.617
## as.factor(property_type)Apartment                           -1.861
## as.factor(property_type)Barn                                -0.284
## as.factor(property_type)Bed and breakfast                   -1.390
## as.factor(property_type)Boat                                 1.659
## as.factor(property_type)Boutique hotel                       2.202
## as.factor(property_type)Bungalow                            -0.177
## as.factor(property_type)Bus                                 -0.058
## as.factor(property_type)Cabin                                0.062
## as.factor(property_type)Camper/RV                           -3.086
## as.factor(property_type)Casa particular (Cuba)              -0.796
## as.factor(property_type)Castle                              -0.358
## as.factor(property_type)Cave                                 0.281
## as.factor(property_type)Condominium                         -0.403
## as.factor(property_type)Cottage                             -1.066
## as.factor(property_type)Dome house                          -0.963
## as.factor(property_type)Earth house                         -2.079
## as.factor(property_type)Farm stay                           -0.705
## as.factor(property_type)Guest suite                         -2.388
## as.factor(property_type)Guesthouse                          -1.402
## as.factor(property_type)Hostel                              -3.536
## as.factor(property_type)Hotel                               -1.026
## as.factor(property_type)House                               -1.785
## as.factor(property_type)Houseboat                            1.992
## as.factor(property_type)Loft                                 0.057
## as.factor(property_type)Nature lodge                        -0.604
## as.factor(property_type)Other                               -0.766
## as.factor(property_type)Resort                               6.607
## as.factor(property_type)Serviced apartment                  -0.998
## as.factor(property_type)Tent                                 0.794
## as.factor(property_type)Tiny house                          -1.763
## as.factor(property_type)Townhouse                           -1.398
## as.factor(property_type)Villa                               -0.096
## as.factor(property_type)Yurt                                -1.166
## as.factor(room_type)Private room                           -67.281
## as.factor(room_type)Shared room                            -35.773
##                                                            Pr(>|t|)    
## (Intercept)                                                3.55e-05 ***
## as.factor(neighbourhood_cleansed)Bedford-Stuyvesant        4.90e-05 ***
## as.factor(neighbourhood_cleansed)Boerum Hill               7.15e-05 ***
## as.factor(neighbourhood_cleansed)Brooklyn Heights           < 2e-16 ***
## as.factor(neighbourhood_cleansed)Bushwick                  3.27e-06 ***
## as.factor(neighbourhood_cleansed)Carroll Gardens           6.64e-05 ***
## as.factor(neighbourhood_cleansed)Chelsea                    < 2e-16 ***
## as.factor(neighbourhood_cleansed)Chinatown                 6.95e-16 ***
## as.factor(neighbourhood_cleansed)Clinton Hill              2.51e-06 ***
## as.factor(neighbourhood_cleansed)Crown Heights             0.011208 *  
## as.factor(neighbourhood_cleansed)Ditmars Steinway          0.689635    
## as.factor(neighbourhood_cleansed)East Elmhurst             0.012603 *  
## as.factor(neighbourhood_cleansed)East Flatbush             5.80e-06 ***
## as.factor(neighbourhood_cleansed)East Harlem               0.000264 ***
## as.factor(neighbourhood_cleansed)East New York             2.07e-05 ***
## as.factor(neighbourhood_cleansed)East Village               < 2e-16 ***
## as.factor(neighbourhood_cleansed)Elmhurst                  0.004471 ** 
## as.factor(neighbourhood_cleansed)Financial District         < 2e-16 ***
## as.factor(neighbourhood_cleansed)Flatbush                  0.002062 ** 
## as.factor(neighbourhood_cleansed)Flushing                  0.009104 ** 
## as.factor(neighbourhood_cleansed)Fort Greene               6.97e-07 ***
## as.factor(neighbourhood_cleansed)Gowanus                   0.006438 ** 
## as.factor(neighbourhood_cleansed)Gramercy                   < 2e-16 ***
## as.factor(neighbourhood_cleansed)Greenpoint                0.000220 ***
## as.factor(neighbourhood_cleansed)Greenwich Village          < 2e-16 ***
## as.factor(neighbourhood_cleansed)Harlem                    0.021313 *  
## as.factor(neighbourhood_cleansed)Hell's Kitchen             < 2e-16 ***
## as.factor(neighbourhood_cleansed)Inwood                    0.041981 *  
## as.factor(neighbourhood_cleansed)Jackson Heights           0.039054 *  
## as.factor(neighbourhood_cleansed)Jamaica                   0.000742 ***
## as.factor(neighbourhood_cleansed)Kensington                0.012394 *  
## as.factor(neighbourhood_cleansed)Kips Bay                   < 2e-16 ***
## as.factor(neighbourhood_cleansed)Long Island City          0.004118 ** 
## as.factor(neighbourhood_cleansed)Lower East Side            < 2e-16 ***
## as.factor(neighbourhood_cleansed)Midtown                    < 2e-16 ***
## as.factor(neighbourhood_cleansed)Morningside Heights       0.022890 *  
## as.factor(neighbourhood_cleansed)Murray Hill                < 2e-16 ***
## as.factor(neighbourhood_cleansed)Nolita                     < 2e-16 ***
## as.factor(neighbourhood_cleansed)Others                    0.058937 .  
## as.factor(neighbourhood_cleansed)Park Slope                4.62e-07 ***
## as.factor(neighbourhood_cleansed)Prospect-Lefferts Gardens 0.000635 ***
## as.factor(neighbourhood_cleansed)Prospect Heights          1.68e-05 ***
## as.factor(neighbourhood_cleansed)Ridgewood                 1.89e-07 ***
## as.factor(neighbourhood_cleansed)Sheepshead Bay            0.040203 *  
## as.factor(neighbourhood_cleansed)SoHo                       < 2e-16 ***
## as.factor(neighbourhood_cleansed)South Slope               0.125883    
## as.factor(neighbourhood_cleansed)Sunnyside                 0.024674 *  
## as.factor(neighbourhood_cleansed)Sunset Park               0.107045    
## as.factor(neighbourhood_cleansed)Theater District           < 2e-16 ***
## as.factor(neighbourhood_cleansed)Tribeca                    < 2e-16 ***
## as.factor(neighbourhood_cleansed)Upper East Side            < 2e-16 ***
## as.factor(neighbourhood_cleansed)Upper West Side            < 2e-16 ***
## as.factor(neighbourhood_cleansed)Washington Heights        0.309414    
## as.factor(neighbourhood_cleansed)West Village               < 2e-16 ***
## as.factor(neighbourhood_cleansed)Williamsburg               < 2e-16 ***
## as.factor(neighbourhood_cleansed)Windsor Terrace           0.594172    
## as.factor(neighbourhood_cleansed)Woodside                  0.045528 *  
## host_since                                                 0.003490 ** 
## Breakfast                                                   < 2e-16 ***
## Kitchen                                                    2.02e-09 ***
## Pets                                                       4.51e-09 ***
## AirCondition                                               0.005588 ** 
## Internet                                                   0.373812    
## TV                                                          < 2e-16 ***
## number_of_reviews                                          0.009095 ** 
## number_of_reviews_ltm                                      1.56e-15 ***
## as.factor(cancellation_policy)moderate                     4.95e-16 ***
## as.factor(cancellation_policy)strict                       0.282340    
## as.factor(cancellation_policy)strict_14_with_grace_period  2.39e-05 ***
## as.factor(cancellation_policy)super_strict_30              5.81e-06 ***
## as.factor(cancellation_policy)super_strict_60              4.84e-09 ***
## guests_included                                             < 2e-16 ***
## beds                                                        < 2e-16 ***
## as.factor(bed_type)Couch                                   0.236176    
## as.factor(bed_type)Futon                                   0.161028    
## as.factor(bed_type)Pull-out Sofa                           0.161329    
## as.factor(bed_type)Real Bed                                0.122473    
## bathrooms                                                   < 2e-16 ***
## as.factor(property_type)Apartment                          0.062735 .  
## as.factor(property_type)Barn                               0.776217    
## as.factor(property_type)Bed and breakfast                  0.164616    
## as.factor(property_type)Boat                               0.097190 .  
## as.factor(property_type)Boutique hotel                     0.027637 *  
## as.factor(property_type)Bungalow                           0.859725    
## as.factor(property_type)Bus                                0.953635    
## as.factor(property_type)Cabin                              0.950474    
## as.factor(property_type)Camper/RV                          0.002029 ** 
## as.factor(property_type)Casa particular (Cuba)             0.425922    
## as.factor(property_type)Castle                             0.720069    
## as.factor(property_type)Cave                               0.778476    
## as.factor(property_type)Condominium                        0.686773    
## as.factor(property_type)Cottage                            0.286260    
## as.factor(property_type)Dome house                         0.335502    
## as.factor(property_type)Earth house                        0.037582 *  
## as.factor(property_type)Farm stay                          0.480849    
## as.factor(property_type)Guest suite                        0.016926 *  
## as.factor(property_type)Guesthouse                         0.160790    
## as.factor(property_type)Hostel                             0.000407 ***
## as.factor(property_type)Hotel                              0.304964    
## as.factor(property_type)House                              0.074343 .  
## as.factor(property_type)Houseboat                          0.046355 *  
## as.factor(property_type)Loft                               0.954239    
## as.factor(property_type)Nature lodge                       0.545875    
## as.factor(property_type)Other                              0.443688    
## as.factor(property_type)Resort                             3.97e-11 ***
## as.factor(property_type)Serviced apartment                 0.318136    
## as.factor(property_type)Tent                               0.427270    
## as.factor(property_type)Tiny house                         0.077851 .  
## as.factor(property_type)Townhouse                          0.162198    
## as.factor(property_type)Villa                              0.923539    
## as.factor(property_type)Yurt                               0.243791    
## as.factor(room_type)Private room                            < 2e-16 ***
## as.factor(room_type)Shared room                             < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 79.97 on 38985 degrees of freedom
##   (15 observations deleted due to missingness)
## Multiple R-squared:  0.5073, Adjusted R-squared:  0.5059 
## F-statistic: 358.4 on 112 and 38985 DF,  p-value: < 2.2e-16
baseline_model <- mean(data1$price)
(RMSE <- sqrt(mean(baseline_model- test$price)^2))
## [1] 0.458891
coefficients(price_model)
##                                                (Intercept) 
##                                               1.050055e+02 
##        as.factor(neighbourhood_cleansed)Bedford-Stuyvesant 
##                                              -1.346971e+01 
##               as.factor(neighbourhood_cleansed)Boerum Hill 
##                                               2.901518e+01 
##          as.factor(neighbourhood_cleansed)Brooklyn Heights 
##                                               6.425522e+01 
##                  as.factor(neighbourhood_cleansed)Bushwick 
##                                              -1.617526e+01 
##           as.factor(neighbourhood_cleansed)Carroll Gardens 
##                                               2.604542e+01 
##                   as.factor(neighbourhood_cleansed)Chelsea 
##                                               8.777666e+01 
##                 as.factor(neighbourhood_cleansed)Chinatown 
##                                               4.485293e+01 
##              as.factor(neighbourhood_cleansed)Clinton Hill 
##                                               2.258685e+01 
##             as.factor(neighbourhood_cleansed)Crown Heights 
##                                              -9.491254e+00 
##          as.factor(neighbourhood_cleansed)Ditmars Steinway 
##                                              -2.375590e+00 
##             as.factor(neighbourhood_cleansed)East Elmhurst 
##                                              -1.835129e+01 
##             as.factor(neighbourhood_cleansed)East Flatbush 
##                                              -2.287327e+01 
##               as.factor(neighbourhood_cleansed)East Harlem 
##                                               1.459749e+01 
##             as.factor(neighbourhood_cleansed)East New York 
##                                              -2.915902e+01 
##              as.factor(neighbourhood_cleansed)East Village 
##                                               5.780695e+01 
##                  as.factor(neighbourhood_cleansed)Elmhurst 
##                                              -1.844210e+01 
##        as.factor(neighbourhood_cleansed)Financial District 
##                                               7.176295e+01 
##                  as.factor(neighbourhood_cleansed)Flatbush 
##                                              -1.411639e+01 
##                  as.factor(neighbourhood_cleansed)Flushing 
##                                              -1.377921e+01 
##               as.factor(neighbourhood_cleansed)Fort Greene 
##                                               2.498015e+01 
##                   as.factor(neighbourhood_cleansed)Gowanus 
##                                               1.726996e+01 
##                  as.factor(neighbourhood_cleansed)Gramercy 
##                                               7.034543e+01 
##                as.factor(neighbourhood_cleansed)Greenpoint 
##                                               1.473227e+01 
##         as.factor(neighbourhood_cleansed)Greenwich Village 
##                                               9.106886e+01 
##                    as.factor(neighbourhood_cleansed)Harlem 
##                                               7.915236e+00 
##            as.factor(neighbourhood_cleansed)Hell's Kitchen 
##                                               7.500068e+01 
##                    as.factor(neighbourhood_cleansed)Inwood 
##                                              -1.292462e+01 
##           as.factor(neighbourhood_cleansed)Jackson Heights 
##                                              -1.510089e+01 
##                   as.factor(neighbourhood_cleansed)Jamaica 
##                                              -2.251171e+01 
##                as.factor(neighbourhood_cleansed)Kensington 
##                                              -1.831082e+01 
##                  as.factor(neighbourhood_cleansed)Kips Bay 
##                                               5.977948e+01 
##          as.factor(neighbourhood_cleansed)Long Island City 
##                                               1.429802e+01 
##           as.factor(neighbourhood_cleansed)Lower East Side 
##                                               5.227282e+01 
##                   as.factor(neighbourhood_cleansed)Midtown 
##                                               1.084366e+02 
##       as.factor(neighbourhood_cleansed)Morningside Heights 
##                                               1.285209e+01 
##               as.factor(neighbourhood_cleansed)Murray Hill 
##                                               6.967759e+01 
##                    as.factor(neighbourhood_cleansed)Nolita 
##                                               8.684186e+01 
##                    as.factor(neighbourhood_cleansed)Others 
##                                              -6.101992e+00 
##                as.factor(neighbourhood_cleansed)Park Slope 
##                                               2.498743e+01 
## as.factor(neighbourhood_cleansed)Prospect-Lefferts Gardens 
##                                              -1.661415e+01 
##          as.factor(neighbourhood_cleansed)Prospect Heights 
##                                               2.433667e+01 
##                 as.factor(neighbourhood_cleansed)Ridgewood 
##                                              -2.746752e+01 
##            as.factor(neighbourhood_cleansed)Sheepshead Bay 
##                                              -1.537743e+01 
##                      as.factor(neighbourhood_cleansed)SoHo 
##                                               1.193304e+02 
##               as.factor(neighbourhood_cleansed)South Slope 
##                                               9.180979e+00 
##                 as.factor(neighbourhood_cleansed)Sunnyside 
##                                              -1.243553e+01 
##               as.factor(neighbourhood_cleansed)Sunset Park 
##                                              -8.643831e+00 
##          as.factor(neighbourhood_cleansed)Theater District 
##                                               9.228847e+01 
##                   as.factor(neighbourhood_cleansed)Tribeca 
##                                               1.659250e+02 
##           as.factor(neighbourhood_cleansed)Upper East Side 
##                                               4.597892e+01 
##           as.factor(neighbourhood_cleansed)Upper West Side 
##                                               5.518816e+01 
##        as.factor(neighbourhood_cleansed)Washington Heights 
##                                              -4.261390e+00 
##              as.factor(neighbourhood_cleansed)West Village 
##                                               1.060733e+02 
##              as.factor(neighbourhood_cleansed)Williamsburg 
##                                               2.791361e+01 
##           as.factor(neighbourhood_cleansed)Windsor Terrace 
##                                              -4.090477e+00 
##                  as.factor(neighbourhood_cleansed)Woodside 
##                                              -1.287384e+01 
##                                                 host_since 
##                                              -1.627348e-03 
##                                                  Breakfast 
##                                               1.364022e+01 
##                                                    Kitchen 
##                                              -9.411602e+00 
##                                                       Pets 
##                                               6.244216e+00 
##                                               AirCondition 
##                                               3.323862e+00 
##                                                   Internet 
##                                               8.808001e-01 
##                                                         TV 
##                                               1.151012e+01 
##                                          number_of_reviews 
##                                              -4.101610e-02 
##                                      number_of_reviews_ltm 
##                                              -3.419255e-01 
##                     as.factor(cancellation_policy)moderate 
##                                              -9.294667e+00 
##                       as.factor(cancellation_policy)strict 
##                                              -6.082563e+01 
##  as.factor(cancellation_policy)strict_14_with_grace_period 
##                                              -4.209245e+00 
##              as.factor(cancellation_policy)super_strict_30 
##                                              -8.605045e+01 
##              as.factor(cancellation_policy)super_strict_60 
##                                               4.943350e+01 
##                                            guests_included 
##                                               1.229666e+01 
##                                                       beds 
##                                               1.899331e+01 
##                                   as.factor(bed_type)Couch 
##                                               1.547594e+01 
##                                   as.factor(bed_type)Futon 
##                                              -1.199636e+01 
##                           as.factor(bed_type)Pull-out Sofa 
##                                              -1.235107e+01 
##                                as.factor(bed_type)Real Bed 
##                                              -1.058017e+01 
##                                                  bathrooms 
##                                               4.725458e+01 
##                          as.factor(property_type)Apartment 
##                                              -4.504420e+01 
##                               as.factor(property_type)Barn 
##                                              -2.376750e+01 
##                  as.factor(property_type)Bed and breakfast 
##                                              -3.604807e+01 
##                               as.factor(property_type)Boat 
##                                               5.979156e+01 
##                     as.factor(property_type)Boutique hotel 
##                                               5.532361e+01 
##                           as.factor(property_type)Bungalow 
##                                              -5.200307e+00 
##                                as.factor(property_type)Bus 
##                                              -4.859472e+00 
##                              as.factor(property_type)Cabin 
##                                               2.907508e+00 
##                          as.factor(property_type)Camper/RV 
##                                              -1.056458e+02 
##             as.factor(property_type)Casa particular (Cuba) 
##                                              -4.155418e+01 
##                             as.factor(property_type)Castle 
##                                              -2.995574e+01 
##                               as.factor(property_type)Cave 
##                                               1.730750e+01 
##                        as.factor(property_type)Condominium 
##                                              -9.800311e+00 
##                            as.factor(property_type)Cottage 
##                                              -5.561619e+01 
##                         as.factor(property_type)Dome house 
##                                              -5.925228e+01 
##                        as.factor(property_type)Earth house 
##                                              -1.084893e+02 
##                          as.factor(property_type)Farm stay 
##                                              -4.340543e+01 
##                        as.factor(property_type)Guest suite 
##                                              -5.897736e+01 
##                         as.factor(property_type)Guesthouse 
##                                              -3.761633e+01 
##                             as.factor(property_type)Hostel 
##                                              -9.384919e+01 
##                              as.factor(property_type)Hotel 
##                                              -2.553606e+01 
##                              as.factor(property_type)House 
##                                              -4.327916e+01 
##                          as.factor(property_type)Houseboat 
##                                               1.039297e+02 
##                               as.factor(property_type)Loft 
##                                               1.395668e+00 
##                       as.factor(property_type)Nature lodge 
##                                              -5.047377e+01 
##                              as.factor(property_type)Other 
##                                              -1.948960e+01 
##                             as.factor(property_type)Resort 
##                                               1.730388e+02 
##                 as.factor(property_type)Serviced apartment 
##                                              -2.445414e+01 
##                               as.factor(property_type)Tent 
##                                               3.712826e+01 
##                         as.factor(property_type)Tiny house 
##                                              -5.362854e+01 
##                          as.factor(property_type)Townhouse 
##                                              -3.397475e+01 
##                              as.factor(property_type)Villa 
##                                              -2.874806e+00 
##                               as.factor(property_type)Yurt 
##                                              -7.172294e+01 
##                           as.factor(room_type)Private room 
##                                              -6.588109e+01 
##                            as.factor(room_type)Shared room 
##                                              -1.001208e+02

Following are the results that we obtained from the above linear regression model along with the hypothesis/explanations of behaviors of the variables:

Based on the p-values for the above regression model, we can say that amenities like TV and Air Conditioning significantly impact the prices. Since the Beta-values are positive, we can say that TV and AC have positive effects on prices (considering other variables are fixed). This can be because AC and TV are comparatively expensive amenties

Also, the booking rate is inversely proportional to the prices considering the other factors remain unchanged Number of guests, number of beds, and number of bathrooms directly affect the prices. As the number of guests, beds or bathrooms increases, prices increase (considering amenities/comfort and location is same). This is reasonable and hotels share the same theory.

The categorical variables: neighbourhood, property type, and room type have significant impacts on prices. This is generally true as properties in luxurious localities will be more expensive than other housing areas where provide similar services. Also, a shared room will be cheaper than a whole house/apartment in the same locality.**

XGBoost

target_train=train$price
target_test=test$price
str(target_train)
##  num [1:39113] 55 200 60 95 100 90 75 90 200 55 ...
str(train)
## 'data.frame':    39113 obs. of  62 variables:
##  $ neighbourhood_cleansed                      : chr  "Others" "Fort Greene" "Washington Heights" "Williamsburg" ...
##  $ id                                          : int  27510869 28289858 7973933 9546695 7680750 14265052 5524950 22987769 21241829 29584983 ...
##  $ scrape_id                                   : num  2.02e+13 2.02e+13 2.02e+13 2.02e+13 2.02e+13 ...
##  $ last_scraped                                : chr  "2019-07-09" "2019-07-08" "2019-07-08" "2019-07-08" ...
##  $ summary                                     : chr  "Newly renovated brick building with old world charm. Cozy room with private bathroom in third  floor apartment." "This immaculate two bedroom one bath is conveniently located in the very desirable Ft. Green/ Clinton Hill sect"| __truncated__ "Spacious bedroom in a large well designed apartment. Two large windows and a large closet. Room is closest to t"| __truncated__ "Just steps from it all, this sunny south facing apt has everything you need for a comfortable stay in stylish W"| __truncated__ ...
##  $ experiences_offered                         : chr  "none" "none" "none" "none" ...
##  $ host_id                                     : int  113295877 213647282 42082221 34000566 347642 33518883 18950563 13878635 78414842 53413881 ...
##  $ host_since                                  : 'difftime' num  105 -483 628 719 ...
##   ..- attr(*, "units")= chr "days"
##  $ host_response_time                          : chr  "within an hour" "within an hour" "within an hour" "within an hour" ...
##  $ host_response_rate                          : num  1 0.99 0.99 0.99 1 0.99 1 1 0.99 0.99 ...
##  $ host_acceptance_rate                        : chr  "N/A" "N/A" "N/A" "N/A" ...
##  $ host_is_superhost                           : Factor w/ 3 levels "","f","t": 2 2 2 2 2 2 2 2 2 2 ...
##  $ host_listings_count                         : int  3 1 1 1 5 2 1 2 1 1 ...
##  $ host_identity_verified                      : Factor w/ 3 levels "","f","t": 2 2 3 3 3 3 3 2 2 3 ...
##  $ neighbourhood                               : chr  "Staten Island" "Brooklyn" "Manhattan" "Williamsburg" ...
##  $ neighbourhood_group_cleansed                : chr  "Staten Island" "Brooklyn" "Manhattan" "Brooklyn" ...
##  $ city                                        : chr  "Staten Island" "Brooklyn" "New York" "Brooklyn" ...
##  $ state                                       : chr  "NY" "NY" "NY" "NY" ...
##  $ smart_location                              : chr  "Staten Island, NY" "Brooklyn, NY" "New York, NY" "Brooklyn, NY" ...
##  $ country_code                                : chr  "US" "US" "US" "US" ...
##  $ country                                     : chr  "United States" "United States" "United States" "United States" ...
##  $ latitude                                    : num  40.6 40.7 40.8 40.7 40.7 ...
##  $ longitude                                   : num  -74.1 -74 -73.9 -74 -73.9 ...
##  $ is_location_exact                           : Factor w/ 2 levels "f","t": 2 2 1 1 2 2 2 1 2 2 ...
##  $ property_type                               : Factor w/ 36 levels "Aparthotel","Apartment",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ room_type                                   : Factor w/ 3 levels "Entire home/apt",..: 2 1 2 1 1 2 1 2 2 2 ...
##  $ accommodates                                : int  2 5 2 2 4 2 2 1 2 2 ...
##  $ bathrooms                                   : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ bedrooms                                    : num  1 2 1 1 2 1 1 1 1 1 ...
##  $ beds                                        : num  1 4 1 1 3 1 1 1 1 0 ...
##  $ bed_type                                    : Factor w/ 5 levels "Airbed","Couch",..: 5 5 5 5 5 5 5 5 5 5 ...
##  $ price                                       : num  55 200 60 95 100 90 75 90 200 55 ...
##  $ guests_included                             : int  1 1 2 1 4 1 1 1 1 1 ...
##  $ extra_people                                : num  5 0 50 0 30 0 30 0 0 15 ...
##  $ minimum_nights                              : int  3 2 1 2 3 1 20 2 3 2 ...
##  $ maximum_nights                              : int  1125 7 1125 1125 45 1125 1125 1125 1125 1125 ...
##  $ minimum_minimum_nights                      : int  3 2 1 2 3 1 20 2 3 2 ...
##  $ maximum_minimum_nights                      : int  3 2 1 2 3 1 20 2 3 2 ...
##  $ minimum_maximum_nights                      : int  1125 7 1125 1125 45 1125 1125 1125 1125 1125 ...
##  $ maximum_maximum_nights                      : int  1125 7 1125 1125 45 1125 1125 1125 1125 1125 ...
##  $ minimum_nights_avg_ntm                      : num  3 2 1 2 3 1 20 2 3 2 ...
##  $ maximum_nights_avg_ntm                      : num  1125 7 1125 1125 45 ...
##  $ calendar_updated                            : Factor w/ 92 levels "1 week ago","10 months ago",..: 13 52 23 45 12 18 14 14 65 26 ...
##  $ has_availability                            : chr  "t" "t" "t" "t" ...
##  $ availability_30                             : int  4 0 0 0 3 0 0 3 0 0 ...
##  $ availability_60                             : int  34 0 0 0 3 0 2 33 0 0 ...
##  $ availability_90                             : int  64 0 0 0 7 0 10 63 0 0 ...
##  $ availability_365                            : int  154 0 0 0 220 0 100 338 0 0 ...
##  $ number_of_reviews                           : int  6 13 3 0 120 15 2 12 9 4 ...
##  $ number_of_reviews_ltm                       : int  6 13 0 0 30 0 1 2 3 4 ...
##  $ instant_bookable                            : Factor w/ 2 levels "f","t": 2 1 1 1 2 2 1 2 1 1 ...
##  $ is_business_travel_ready                    : chr  "f" "f" "f" "f" ...
##  $ cancellation_policy                         : Factor w/ 6 levels "flexible","moderate",..: 2 4 1 1 4 4 4 1 2 4 ...
##  $ calculated_host_listings_count_entire_homes : int  0 1 0 1 2 0 1 0 0 0 ...
##  $ calculated_host_listings_count_private_rooms: int  3 0 1 0 1 2 0 2 1 1 ...
##  $ calculated_host_listings_count_shared_rooms : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ TV                                          : num  0 1 1 1 1 1 0 1 1 0 ...
##  $ Internet                                    : num  0 0 1 1 1 1 1 0 0 0 ...
##  $ AirCondition                                : num  1 1 1 1 1 1 1 0 1 1 ...
##  $ Pets                                        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Kitchen                                     : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ Breakfast                                   : num  1 1 0 0 0 0 0 0 0 0 ...
train$price=NULL
feature_names <- names(train)
test$price=NULL
dtrain <- xgb.DMatrix(as.matrix(sapply(train, as.numeric)),label=target_train, missing=NA)
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
dtest <- xgb.DMatrix(as.matrix(sapply(test, as.numeric)),label=target_test, missing=NA)
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion

Set up cross-validation scheme (3-fold)

foldsCV <- createFolds(target_train, k=7, list=TRUE, returnTrain=FALSE)
param <- list(booster = "gblinear"
              , objective = "reg:linear"
              , subsample = 0.7
              , max_depth = 5
              , colsample_bytree = 0.7
              , eta = 0.037
              , eval_metric = 'mae'
              , base_score = 0.012 #average
              , min_child_weight = 100)

xgb_cv <- xgb.cv(data=dtrain,
                 params=param,
                 nrounds=100,
                 prediction=TRUE,
                 maximize=FALSE,
                 folds=foldsCV,
                 early_stopping_rounds = 30,
                 print_every_n = 5)
## [1]  train-mae:73.672862+0.111883    test-mae:73.678424+0.805665 
## Multiple eval metrics are present. Will use test_mae for early stopping.
## Will train until test_mae hasn't improved in 30 rounds.
## 
## [6]  train-mae:71.617013+0.196163    test-mae:71.626800+0.853066 
## [11] train-mae:68.533311+0.181592    test-mae:68.547896+0.831818 
## [16] train-mae:66.164162+0.178459    test-mae:66.182204+0.808597 
## [21] train-mae:64.476784+0.174080    test-mae:64.500718+0.792143 
## [26] train-mae:63.206394+0.177173    test-mae:63.231299+0.783250 
## [31] train-mae:62.232872+0.175572    test-mae:62.258859+0.784180 
## [36] train-mae:61.470202+0.175628    test-mae:61.496858+0.790255 
## [41] train-mae:60.854143+0.172496    test-mae:60.880601+0.798381 
## [46] train-mae:60.345085+0.176652    test-mae:60.372223+0.798627 
## [51] train-mae:59.921587+0.177307    test-mae:59.950626+0.801231 
## [56] train-mae:59.564381+0.175756    test-mae:59.594660+0.804870 
## [61] train-mae:59.256600+0.175789    test-mae:59.287972+0.806301 
## [66] train-mae:58.985555+0.174694    test-mae:59.018218+0.808247 
## [71] train-mae:58.745945+0.174260    test-mae:58.779264+0.810722 
## [76] train-mae:58.533288+0.174144    test-mae:58.566710+0.813143 
## [81] train-mae:58.339680+0.173086    test-mae:58.373902+0.815930 
## [86] train-mae:58.163766+0.172355    test-mae:58.199008+0.817836 
## [91] train-mae:58.003126+0.172493    test-mae:58.039364+0.818647 
## [96] train-mae:57.854376+0.171417    test-mae:57.891479+0.819875 
## [100]    train-mae:57.742967+0.171288    test-mae:57.780617+0.819684

Check best results and get best nrounds

print(xgb_cv$evaluation_log[which.min(xgb_cv$evaluation_log$test_mae_mean)])
##    iter train_mae_mean train_mae_std test_mae_mean test_mae_std
## 1:  100       57.74297     0.1712877      57.78062    0.8196839
nrounds <- xgb_cv$best_iteration

xgb <- xgb.train(params = param
                 , data = dtrain
                 # , watchlist = list(train = dtrain)
                 , nrounds = nrounds
                 , verbose = 1
                 , print_every_n = 5
                 #, feval = amm_mae
)

Feature Importance

importance_matrix <- xgb.importance(feature_names,model=xgb)
xgb.plot.importance(importance_matrix[1:15,])

head(test)

Predict

preds <- predict(xgb,dtest)
preds
##    [1]    88.880173    99.294037   144.362793   117.910576    85.451347
##    [6]    97.283577   219.103470    77.462975   163.462601   159.818100
##   [11]   169.157852   416.755280   126.675903   203.280579   142.627441
##   [16]   250.977692   152.276611   219.252670   167.497559   314.249878
##   [21]   189.745117   191.274658   147.411789    94.378220    99.858559
##   [26]   141.798981   150.004089    74.939888   293.959167   131.730469
##   [31]   175.831131   126.955063   112.523193    47.001705    91.929718
##   [36]   143.569275   158.944931   256.066223    79.791061    94.278687
##   [41]   117.194641   130.682587    82.783455   197.966446   187.734222
##   [46]    87.860672    80.071960   152.438065   162.253433   185.950348
##   [51]   125.543045    98.419861   133.362137   176.997437   178.821976
##   [56]   110.297058   221.662643   127.133934   161.428650   131.290619
##   [61]    69.073967   176.573639   153.242828   103.154778    95.349892
##   [66]   170.783585    80.206551   122.467125    76.180305   145.304626
##   [71]   142.322266    78.873466   108.047173   121.531326    86.681221
##   [76]   168.126221   195.360031    50.426228   270.346802    62.448090
##   [81]   138.070068   184.043350   139.737122    82.550743   157.040588
##   [86]   134.906006   104.806168    93.089058   113.384018    72.620003
##   [91]    79.966545    90.062119   206.938324    70.459534   103.165649
##   [96]   119.265434   134.291626    94.136917   108.229660   127.046341
##  [101]    86.306793   129.043198   167.381348    68.831764    79.453056
##  [106]    72.718208   146.962463    93.334297   149.676331   139.543106
##  [111]   155.459305   102.892136   175.964355   150.554413   104.050072
##  [116]   118.834366   137.258987   107.832603    76.040321   128.698593
##  [121]   127.007019    82.340477   259.747955    92.751595   136.392838
##  [126]   238.168823   125.119316    77.584740   115.657845    80.093697
##  [131]    86.628738    56.567238   129.805191   161.675476   107.924248
##  [136]    85.821152   128.810684    79.589272   116.724289   101.043015
##  [141]    63.461861   135.744095   228.609436   115.947762   181.738739
##  [146]    62.741898    88.714157   238.467438   112.320190   127.840126
##  [151]   141.322800    69.163437   122.844009   146.221756    76.232269
##  [156]   146.212830   328.082520   137.772659   131.789368   123.303543
##  [161]   103.356064   202.647369   111.457825    89.852554   141.763275
##  [166]   109.407860   164.635239   119.612343   113.276833   124.300781
##  [171]   166.098969   106.900345   208.474197   133.856598   245.171738
##  [176]    97.068756   286.366394    89.501579   134.775162   108.643883
##  [181]   104.438141   189.853073   130.557114    98.555130   278.044006
##  [186]    74.124847    82.521721   116.186806   148.699615   150.936035
##  [191]   129.187180   139.460785    96.058685   116.602783   245.479187
##  [196]    91.657227    78.637245   394.926666   157.972122   175.508469
##  [201]   102.224632   140.708374   210.728806   185.791855   191.582809
##  [206]   264.669891   281.234955   314.304871   201.978653   209.908875
##  [211]   144.289581   166.296417   102.216171   112.229683   105.092522
##  [216]   121.114990   233.957123   121.993675   175.158310   100.923233
##  [221]   105.068695    88.035851   122.417534    86.670357   106.604218
##  [226]   113.199875   140.224564   173.132584    98.660667    77.997093
##  [231]   186.424927   -26.158016    96.932190    96.270409   205.965927
##  [236]   101.289452   -26.916622   -18.400436   161.874237   162.949829
##  [241]    69.052811   112.296577   275.569427   322.637054   119.295074
##  [246]   124.612755   126.437447   216.252548   532.605347   152.624481
##  [251]   135.467255   249.222626   152.866867   107.256325    49.825199
##  [256]    85.618683    60.907810    89.945633    90.644905   151.878235
##  [261]   274.741547    78.509636   148.606888   139.121246   108.925316
##  [266]   136.071518   119.371574   244.123550   124.460541   134.768814
##  [271]    72.725136    82.106262   144.193680   144.210785   116.096161
##  [276]   223.606308   228.135544    93.412964    55.853092    86.304504
##  [281]   171.455887   186.317017   225.808624   130.199081   196.228638
##  [286]    95.929459   168.943237   158.669220   118.337799    95.945274
##  [291]   130.171890    93.103073   350.211639   175.767075   102.934288
##  [296]   198.338684    95.281761   142.867462    53.452564   153.710648
##  [301]   187.131042   218.766129    90.125145   120.339951   147.475601
##  [306]   153.712311   222.972366   129.414978   127.928772    79.718666
##  [311]   214.644135   183.525192   117.842201   229.033600   103.434738
##  [316]   268.917633    91.764664    76.130859   125.786224   165.318176
##  [321]   114.809723   112.137825   177.021393    86.699860   134.851990
##  [326]   215.999466   156.135925    75.834251    79.948204    81.415497
##  [331]    83.066635   123.663033   257.530060   140.946106   146.703705
##  [336]   111.089859   243.571030    80.150948   114.139893   116.740860
##  [341]   154.745087   191.168793   505.400909   228.535675   188.646652
##  [346]   137.274704   132.709824    90.442101   139.363388   117.081894
##  [351]   146.176224    91.781303   124.746078    63.457256   285.237000
##  [356]   117.758263   115.900452    92.043571   103.605942   111.839439
##  [361]   240.076035   231.977036   111.112007    99.690331   129.342422
##  [366]    67.026558    65.908043   211.236298    88.242249   104.935333
##  [371]   259.144104    73.762810   210.073257   196.429398   282.528656
##  [376]   111.366821   124.906296   139.240311   101.631264   108.422127
##  [381]   131.176208    97.567879    48.536404   104.368507   106.569633
##  [386]    89.984520   117.685211   242.711441   252.618179   122.683960
##  [391]   104.140472   109.727852   304.216583    92.967598    86.359329
##  [396]   289.156158    81.349319    79.193993   131.358917   132.562286
##  [401]    82.373299   104.863976   161.994522    98.862846    81.815323
##  [406]   118.928421   157.762466   212.226791    92.590546   105.256149
##  [411]   206.820480    89.902733    97.049469   149.433609   104.890602
##  [416]   100.418724   175.636414    85.100662   119.220970   153.985107
##  [421]    92.388168   221.401978   134.495361    94.740768   110.789978
##  [426]   319.077911   139.008530   131.571213   157.898178   130.680878
##  [431]   162.772400    87.887291   101.271233   161.122726   205.366486
##  [436]   156.153641    88.978485   140.182800   143.890686   172.896362
##  [441]   103.814964   176.208130   174.173172    82.135414   176.072128
##  [446]    91.511200    72.653259   140.413132   164.445999   166.926422
##  [451]    83.636589   289.235077   192.916824    63.447708   156.014832
##  [456]   151.170883   318.497864   108.164810    78.145805   128.793488
##  [461]    85.436951   176.279800   109.969307   112.320755    94.622475
##  [466]   257.013123   138.056381    77.007492   104.938759   113.061234
##  [471]   126.243111   251.849167   103.950546   153.699265    97.442657
##  [476]   108.857925   210.745743   156.867294    95.489029   141.524429
##  [481]   108.652473   193.493378   243.546783    80.876198   143.049103
##  [486]   125.900070   164.093307   117.118042   193.443420   225.717743
##  [491]    92.701439   120.109230   125.822372   108.791039    92.557648
##  [496]    98.195190   142.477921   112.036568   107.318329   171.583694
##  [501]   218.633087   176.085846   191.226501   115.589813   134.612915
##  [506]   131.637833   114.034462    91.300148   102.438370    80.704773
##  [511]   130.013779    98.640869   140.354919   396.503632   161.256973
##  [516]   108.132805    92.535660    87.775810   101.684021    84.295784
##  [521]   181.059647   155.007645   158.328705    93.556602   100.527611
##  [526]   100.373619    56.551857   133.125809   187.523590    92.691673
##  [531]   318.986816   117.130333   116.514206   146.301544    90.406281
##  [536]   343.949249   154.828156    73.094086    91.550018   213.225174
##  [541]   126.898270   157.964844   111.233322   253.486176   112.540611
##  [546]    83.315277    68.864136   169.579102    95.841858   135.618042
##  [551]    83.907692   108.744713   121.846802    85.665405   154.449997
##  [556]   233.836121    92.323982   331.429871   294.065155   173.813583
##  [561]   166.704193   125.089218   142.580795    82.534561   137.784546
##  [566]   104.040207    72.220390   121.409332    84.333557   196.224442
##  [571]   158.581299   138.598785    90.401100   103.522316    84.107666
##  [576]   139.225174    83.179497    -7.446103   102.100433   140.630280
##  [581]   129.831024   102.478333   150.088104   -25.957058   136.919174
##  [586]   188.128891    67.602715   194.475388   134.793640   201.616440
##  [591]   100.200531   141.606277    81.994385    76.956169   160.615753
##  [596]   108.332436   168.247971   105.180031   154.595367   131.245178
##  [601]   104.211403   124.426231    99.906044   270.411041    73.699203
##  [606]   148.336166   106.124893   120.591057   119.241020   111.631943
##  [611]   197.797424   133.142990    72.608696    58.973881   164.623093
##  [616]   120.881454   217.360626   107.730881   187.705017   113.262466
##  [621]   103.900146   169.497269   100.102829   307.928406    83.129051
##  [626]    90.767509    85.643105   110.290047    77.580254   128.338013
##  [631]   106.993423    71.000526   119.820488    87.798325   103.730583
##  [636]    90.766960   113.507187   128.332840   141.496445   103.730072
##  [641]   170.806473   194.863937   293.350861   242.600433   100.801918
##  [646]    94.852310   115.289207   132.582825    88.866669   142.590866
##  [651]   152.893799   160.302383   165.444901    89.686005   143.954086
##  [656]   210.235413    85.698410   217.345474   106.194084   -13.478653
##  [661]   241.587662   100.422211   204.364532    69.639137   193.152634
##  [666]   146.767197   201.577332   128.217270   128.101410    67.998421
##  [671]   109.920181    82.940475   155.970718    96.550079   168.322540
##  [676]   166.595901    87.295570   158.843887    86.200401   253.147263
##  [681]    82.548653    62.883087    71.633812    67.067337   190.356918
##  [686]   165.506119   116.657639   311.911133    88.012245    75.194893
##  [691]   104.314293   143.210892   253.065811    99.591011    99.880547
##  [696]   217.425507    88.959808   193.376419    80.635735   163.574203
##  [701]   134.865997   304.360046   135.256317    76.548943   268.987640
##  [706]    82.361526   145.021393   120.412209   100.004364   210.468399
##  [711]   198.246613    92.324837   207.559952   172.510117   219.589172
##  [716]    78.652184    96.996521   129.443039    81.253944   185.382690
##  [721]   113.207687   111.951157    97.919678    96.204323   207.384277
##  [726]   116.552254   139.253754   176.122681   149.088821    86.445900
##  [731]   124.497047    99.489723   133.467087    90.911003   102.229988
##  [736]   112.939514   252.694443   191.705261   112.802109    89.983017
##  [741]   247.403549   133.483673    90.850891   135.493744    65.332031
##  [746]   407.406372   103.956940    86.916603   162.920776   100.891068
##  [751]   165.435059   126.422234   160.098007   206.372162    41.062298
##  [756]    75.540947   222.513901    46.821636   103.432846    83.898109
##  [761]   201.826630    73.515579   106.820694   111.103653   217.012070
##  [766]    86.443596   161.170166   154.047165   350.973145   128.851196
##  [771]   136.101654   194.595703    75.818565    74.893349   200.386063
##  [776]   134.458588   113.974480   309.637329   170.622177    74.875801
##  [781]    73.086250   -22.262100   100.074532   155.020264   157.922989
##  [786]    83.245560    85.452446   134.229858    90.670624    31.611259
##  [791]   140.783310   160.375443    89.759048   104.556709    88.063202
##  [796]   121.674454   206.485886   103.012115    46.299995   110.644447
##  [801]   283.709747   165.541183   141.005814   147.200378   118.498764
##  [806]    84.268471   147.523529   105.630447   140.658859   116.048180
##  [811]   143.405380    88.288940   175.614319    99.414009   132.435944
##  [816]   184.169617   186.555710    98.560806   131.762970   205.517944
##  [821]    82.251595   172.717529   142.625839    78.026085   311.858948
##  [826]   123.027672   190.376846   100.274536    63.523708   190.464081
##  [831]   114.286362   193.270737    69.016106   120.193672   155.522766
##  [836]    85.181175    83.810432   218.670258   337.082153    67.716347
##  [841]   399.641663    87.928780   173.711487   110.278976    95.653046
##  [846]    86.244865   138.936981    62.899437   160.975708   108.504166
##  [851]   147.921463   205.485840   305.044708   332.525055   240.748901
##  [856]   209.913620   126.963875   126.633904   245.628738   127.302879
##  [861]    99.332558    87.303040   124.908684   150.230850   148.650604
##  [866]    95.391937    89.299324    80.809891   117.053391    90.285034
##  [871]   150.653931   202.910858   136.078171   247.380783   117.643799
##  [876]    85.316963   112.598312   118.500008   188.598389   186.766693
##  [881]    83.721451    81.558006    91.017281    94.414665    83.342690
##  [886]   145.848068    71.217743   181.374786    59.330769   322.877228
##  [891]    96.269051   140.508194   140.577026   180.684891   165.387100
##  [896]   106.515228    69.204720   226.787018   132.439667    71.475395
##  [901]    85.837006   129.527603   111.317734   109.097809   131.012802
##  [906]   218.102707   165.409851   109.021065   307.250000    89.005386
##  [911]   134.321548    72.238564    81.364433   102.312767    82.162445
##  [916]   -20.404491   202.628021    96.420845    73.777695    74.576172
##  [921]    78.816628   114.492081   243.581589   101.592148   157.090698
##  [926]   115.193642   152.256409    61.536156   142.934982   179.859512
##  [931]   107.763870   216.773788   102.698868   201.910568   113.347656
##  [936]    88.779877    85.846176   117.987289   100.877625   146.052765
##  [941]   151.595108   132.911194   169.970490   119.455261    69.266670
##  [946]   228.942535   149.256592   107.796249   157.717087   118.321442
##  [951]    72.808708   187.056274   119.095306   129.740326   121.657516
##  [956]   125.518341   224.239807   121.858986    61.856380   238.507812
##  [961]   117.128883   114.608315   145.076767   118.360970   124.237320
##  [966]    82.301208   121.095505    76.062561   142.580215   163.877014
##  [971]   253.354797   168.234665   244.172531   199.220871   190.808487
##  [976]    91.734337   314.280212   196.610794    92.090797    85.212242
##  [981]   189.458450    74.070213    28.477516   112.599869   100.417091
##  [986]   151.258026   114.975357   155.061111   165.094040   221.840668
##  [991]   132.320831   116.934128    80.382065   195.470306   137.334122
##  [996]   227.376999   325.103058    99.612762   122.651512   553.232056
## [1001]   119.542984   108.378372   106.145821   144.068512   116.799805
## [1006]   166.833527   106.747231   103.634293   431.922729   122.593163
## [1011]    88.870316   157.487305   118.822678   106.770889   210.137589
## [1016]   105.332718   148.738159    95.648132   145.793640   197.668243
## [1021]    77.363472   118.029236   110.887787    89.990250   122.175049
## [1026]   209.233170   140.063278    31.347530   223.033875   112.254120
## [1031]   236.330460   110.113388   122.091385   324.407074   287.910828
## [1036]   217.212372    96.218735   142.016220   169.750626   130.016098
## [1041]    87.894516   152.329407   193.228912   190.484039   212.776489
## [1046]   156.675858   194.243881   196.496094   210.195969    91.322731
## [1051]   152.699203   215.825424   359.705627   274.614105   221.911682
## [1056]    86.143501   143.878754    91.994713   148.982986   144.162872
## [1061]   119.243546    73.362221   115.111832   200.828751   200.512955
## [1066]   105.926178   128.568176   210.054321    87.588623   171.612106
## [1071]   213.871094   132.440186    89.513046   112.152641    83.422783
## [1076]    55.984093   130.966904   112.116814   132.913315   108.434875
## [1081]    72.003662    99.810059   102.744919   134.618668   102.260704
## [1086]   111.839569   278.879913   137.544678    70.140602   123.111519
## [1091]   140.124557    56.395218   118.662292   127.365852    79.127739
## [1096]   103.133026   149.795242   130.800461   133.753052    89.152275
## [1101]   192.421921   167.417496    80.843605    49.318329   159.443268
## [1106]   219.016876   209.068970   154.723129   125.377106   111.253914
## [1111]   132.750916   196.712952   249.909393    99.120377   123.435776
## [1116]   219.602905   163.225586   149.542618   109.014023   141.546066
## [1121]   171.372406   113.322624   104.304451    85.738922   105.749504
## [1126]    93.909142   231.891449   107.083664   137.792191   340.467560
## [1131]   122.043137   130.851196   111.039131   142.935913    98.475754
## [1136]   280.364014   103.177956   136.367188   290.523193   175.280075
## [1141]   114.903908   150.043655    81.452118    83.188896   188.148941
## [1146]   103.361572   123.035912   251.731354    68.509605    63.501171
## [1151]   124.328377   205.827362   120.796982   120.062515   150.530853
## [1156]   109.251915   129.731812   126.901680   110.766159   102.170013
## [1161]   116.578217   225.406158   216.426056   106.639046   263.457916
## [1166]    90.803741    91.719482    50.489586   142.483521    69.450180
## [1171]   125.657959    62.784832   130.389236    85.688919    85.353920
## [1176]   177.510803    90.987610   129.266174   127.643646    66.770187
## [1181]    83.518219   131.460556   253.970383    87.374321   102.041992
## [1186]    57.457199   105.040405   100.125786   100.344856   129.225784
## [1191]   118.488907   107.673897    99.530037   100.178261    73.093422
## [1196]    87.426109    94.915871    86.084229   110.128288   105.452621
## [1201]   112.886871   170.248276   117.703789    98.001839   119.195724
## [1206]   138.856506    72.963120   109.490967   176.081787    87.602112
## [1211]    91.852089    94.729767   118.213989   128.380264    83.570351
## [1216]   243.665817    97.050926    96.021492    70.331207    87.950737
## [1221]    89.170006   115.339020   207.350327   102.786331   111.997940
## [1226]   238.566772   105.324562   120.676201   148.057663    75.766655
## [1231]   128.240952   117.875000   147.607162   295.472382   145.815552
## [1236]    88.346184    99.679459    86.564865   110.401283    95.386101
## [1241]    90.131592   509.197296   136.317963   115.342857    98.603119
## [1246]   104.620789   117.127213   153.081055    90.886589   131.440643
## [1251]   109.914139   264.873627   160.576736    73.187248    90.181000
## [1256]   114.028389    92.771072    70.160362   107.808159   258.851288
## [1261]    66.567719   101.653633   107.948524   107.181374    84.715828
## [1266]   184.262268    68.218346   147.511597    79.198593   101.812584
## [1271]    93.668633   215.366287   117.851807   212.702393   116.441437
## [1276]    82.549080    99.927338   146.480270   204.180618   107.484306
## [1281]   126.756508   148.837097   111.084984   109.764610   107.572647
## [1286]   102.026199    50.785141    90.888107   113.667641   110.891724
## [1291]   104.367485   273.807098   154.654419   256.779999   114.383560
## [1296]   175.889572   101.094505   151.738510    97.988724    84.822128
## [1301]   136.729401   163.625504   124.916748   183.845337    90.831970
## [1306]    78.879196   147.579666   121.960152   106.042938    81.422684
## [1311]   215.233383   118.483421    77.140625   208.642868    93.571457
## [1316]   112.058563   137.858414   148.121490   115.106415   103.870934
## [1321]    92.522972   109.494751   117.300819   113.168610    91.160683
## [1326]   136.276230   116.012260    94.909637   118.449593    92.707451
## [1331]    88.812851   203.260040   185.509933   126.195717    92.921249
## [1336]    88.452065   139.886520   112.643692   109.916115   105.213219
## [1341]   111.792618   114.048683    70.763557   109.724640    91.566719
## [1346]   144.902695   131.989182   164.995651   122.366447    78.986481
## [1351]   192.163925   103.390656   106.569687   131.157333   178.318985
## [1356]   109.721581   253.297073   104.509239   275.714874   116.948708
## [1361]   102.674683    83.064873   129.460693   186.284317   158.930481
## [1366]   173.489319   195.865234   191.581421   179.617264    76.160629
## [1371]   117.928017   122.557808   104.070297    90.752350   109.723953
## [1376]    87.943748   153.823624    98.517021   113.023781   130.197128
## [1381]   117.538002   185.476334   190.043320    62.505005    91.754364
## [1386]   116.084290    65.240089   316.953796   191.032608    77.871368
## [1391]   172.016663    54.632103    95.052071   101.847237   136.841370
## [1396]   115.479980   108.073746   170.961151   192.760773   155.091751
## [1401]    87.864784   131.466232   129.525482    89.880676    68.870476
## [1406]   169.000702    81.909164   114.280304    83.486954   224.309082
## [1411]   189.908646   148.443726   237.803665    88.495636   136.045319
## [1416]   127.368744   305.707947   178.272369   145.381714   141.527130
## [1421]   172.299820    74.463150   103.543457    68.044502   128.718216
## [1426]    94.035606   143.811172    89.990623   257.033752    77.783348
## [1431]    68.612686    70.109818    66.828255   262.690521    60.056240
## [1436]   107.078377    82.948738   316.889130   231.120178   136.213486
## [1441]   104.208740    92.565567    93.684570   133.174545    94.159081
## [1446]   107.681152    86.734238   123.500824    34.061768    72.214737
## [1451]   149.400925    99.418167   175.728821   116.654205    79.719597
## [1456]   163.942062   145.899994   278.359589    85.485611   152.851700
## [1461]   147.284546    97.681084   118.527313    92.797195    58.580864
## [1466]   108.732590   184.226364   107.733734   237.641800    91.639313
## [1471]    91.969803    66.372833   229.827408   122.986992   115.560638
## [1476]   117.632156   156.347702   148.258865   129.056030   155.336243
## [1481]    93.236908   152.540344   102.261215   121.816505   118.114220
## [1486]    51.056309    86.643188   153.579758   136.918457   159.444366
## [1491]   141.989349    67.647865    80.069237    63.477604   207.121048
## [1496]    85.716202   132.693085    77.424515    95.449562   108.401596
## [1501]    84.502357    76.417953   130.910568    82.685829   148.155548
## [1506]   235.246597   125.824249    56.611649   101.003494   109.894897
## [1511]   149.220566    87.847763   132.363403   101.591583   112.133400
## [1516]   164.889786   164.078735    89.697357   188.906189   104.068047
## [1521]   109.469887    81.391724   133.438828    91.778633    70.246094
## [1526]   101.625397   164.792145   169.377731   131.565704    96.445831
## [1531]   169.786713   276.673798   123.604439    78.129868   132.694305
## [1536]    84.233253    82.293556    86.076836   176.576508   122.427948
## [1541]   103.313568   100.857864    80.333809   104.100754   196.367264
## [1546]    87.636475   207.210587   113.265633    89.481750    96.960434
## [1551]    63.864822    82.016579    67.446922   139.440079    68.031952
## [1556]   119.909592    99.319275   161.453201   111.166283   161.722137
## [1561]    66.932526    79.786354   164.764755   120.459915   165.449768
## [1566]   172.560242   273.550293   191.547775    83.808647   133.182465
## [1571]   116.158669    88.660591   358.152863    84.126808   264.092133
## [1576]   217.893372    86.457489   179.176132    90.516365   144.360077
## [1581]   184.379379   136.628464   109.033127    98.957672    77.127739
## [1586]    71.814201   111.687119   106.640015    94.799591    97.305214
## [1591]    91.990990   114.277588   175.090576   144.274811   134.466705
## [1596]    84.861954   148.952087   125.264023    71.770966   158.899139
## [1601]    97.539848    73.439873   108.316147   122.386543   426.445801
## [1606]    94.307640   180.171631   158.333527    86.070976   113.289429
## [1611]   104.318474   169.993027    81.039009   332.499786   143.771713
## [1616]    65.598320   102.574661   103.403046   183.259186   120.617409
## [1621]   183.842102    85.837982    88.697838   139.297806   128.783203
## [1626]   145.575211   160.039719   141.008865   119.677872    87.803322
## [1631]    99.511932   141.764465    62.053020   421.910645   162.814041
## [1636]   283.579163   265.942078   235.903534   103.063156   118.794876
## [1641]    99.710594    71.461624   202.556030   146.314011    92.000504
## [1646]   101.954254    90.747772   168.664337   281.537598   142.610321
## [1651]   176.494034   160.418045   132.971802   145.734924   105.467773
## [1656]   144.367676   196.544220   179.632492   176.609512   153.505478
## [1661]    73.599289   180.659851   122.702484   194.092834   142.463089
## [1666]   123.730591   245.847137   103.011711   187.186081   181.428772
## [1671]   179.664246   191.864655   160.625534    66.280449    79.642586
## [1676]   120.090355   129.135391   133.953278   104.481934   129.535965
## [1681]   129.166733   131.229660   124.522980   129.694946    99.361526
## [1686]   191.086746   236.939713   169.137878   186.396591   144.515564
## [1691]   104.487442   115.419899   100.303337   263.054504   192.500778
## [1696]   127.264313   181.274719   124.062057   267.461914   240.470810
## [1701]   128.130981   149.096817    65.156639    84.857498   203.307785
## [1706]   182.275986   103.279587    95.457916   138.222427    73.245094
## [1711]   118.508331   223.154709   199.766296    97.650360   123.469536
## [1716]    70.876953   152.431580   158.636597   167.632187   119.047195
## [1721]    73.462502   202.619797   164.688477    55.766495   149.471176
## [1726]   138.725708    53.128681   207.106125   181.395493   257.873260
## [1731]   239.919861   185.532166    93.761269   303.256714   160.808258
## [1736]   246.692963   206.257706   158.657440   100.927322   299.548096
## [1741]   190.490402   112.524986   121.779419   283.156250   209.189041
## [1746]   157.838303    91.656364   132.116852    44.319118   245.157593
## [1751]   106.817825   143.297104   264.227112   113.725182   279.419373
## [1756]   173.156052   124.357758    88.173294    66.706123   150.311295
## [1761]   107.828781   146.610092   109.050583   120.266449    80.793556
## [1766]   122.961288   150.709213   151.801361   203.941376    58.983685
## [1771]    97.350250   138.064117   123.455162   122.414444   204.101730
## [1776]   233.975861   139.544418   118.109474   160.605087   103.313850
## [1781]   185.732315   149.992065   144.351364    72.738861   117.250183
## [1786]    87.861038    84.701492   125.339348   129.577194    77.539375
## [1791]   177.577072   169.700882    33.838093   155.675262   178.602310
## [1796]   120.804810   241.130402   117.518677   109.373260   312.087677
## [1801]   121.056664   101.933876   137.609222   182.717819    94.071907
## [1806]   258.643890   120.365486   214.523300   258.356934   154.242188
## [1811]   154.815033   297.514587    79.998337   259.588165   101.032104
## [1816]   131.648834   104.302887   144.268753   248.753113   100.822556
## [1821]   269.799500   130.616058   163.094452   138.575150   112.237274
## [1826]   133.491592   248.384064   152.733444    89.012199   533.791748
## [1831]    89.975777   105.323921   169.242767   136.388458   148.480301
## [1836]   234.686935    73.154205    85.119499   129.774170   145.655029
## [1841]   151.412582   181.250198   169.169586   139.675293   123.903336
## [1846]    89.908592   110.128830   129.343475   203.167419   298.713470
## [1851]   120.356033   102.144249   204.619186   148.616821   125.555122
## [1856]   132.642456   101.056686   146.901474   121.428314   142.879791
## [1861]   117.784355   134.754745   276.174652   172.004318   226.815491
## [1866]   116.744003   154.329926    99.783691   122.851822    92.476112
## [1871]   124.584015   289.557739   148.485764   110.348923   385.349243
## [1876]   120.494537   138.515991   156.127060   174.102676   171.436630
## [1881]   351.282471   173.948578   102.037292   152.289597   173.286209
## [1886]   124.826263   114.654022    67.732559   100.937164   302.845612
## [1891]   136.964584   255.179581   106.138580   205.446548   191.986176
## [1896]   135.926254   257.973114    86.861588   123.874344   191.516403
## [1901]   106.527328   277.409180   155.164062   121.295334   148.398666
## [1906]   113.261887   136.250641   104.502724   189.581314   265.849854
## [1911]    88.360649   142.389893   126.364586   202.023514   120.735809
## [1916]   216.104218   165.779373   117.815979   107.976303   158.098343
## [1921]   156.729477   153.521408   121.545425   247.602951    75.525108
## [1926]   126.582779   254.483231    86.341507   163.822556   121.010620
## [1931]   173.688843   174.827621   110.917923   158.328934   116.123566
## [1936]   148.525787    68.974953   136.673203   280.916260   173.019592
## [1941]   201.663879    98.971954   106.857391   128.072891   131.444809
## [1946]   229.963181   137.885773   126.911934   179.641708   421.864563
## [1951]    90.232155   119.216934   207.770966    98.174309   137.517975
## [1956]   159.009460   119.378723   132.883942   164.556061    91.455925
## [1961]   278.431793    72.438271   131.161621   122.818703   141.750183
## [1966]    73.224403   121.563477    65.881187   190.428528   101.528969
## [1971]    82.713165    71.473129   131.655838   157.107635    92.441139
## [1976]   106.087593    87.179939   128.844040    96.653313   164.760208
## [1981]   257.134521   147.208618    81.856415   166.105850    94.557457
## [1986]   106.273186   117.119339   135.290176    80.882378   112.484596
## [1991]   151.121552   184.620331   136.375214    83.814095   174.450287
## [1996]    93.787003    96.184074   151.917694   258.360016   200.384598
## [2001]    76.839569   112.963623   136.598709    96.371887   167.444946
## [2006]   236.927017   133.382538    97.365814   111.237350    65.783119
## [2011]   186.329285    46.939510   134.923248   175.231415   124.190430
## [2016]   114.759888   131.963623    84.288200    99.447144   220.013367
## [2021]    77.470978   276.062836   114.177269   122.265671   181.817902
## [2026]    67.821793    92.761086   142.152039   193.330536    64.863068
## [2031]   133.564911   148.379654   134.054733   357.552582   255.326767
## [2036]   125.926056   204.021057   108.340088   234.538467   133.555161
## [2041]    95.896675   422.037476   101.235092   193.706955   104.194656
## [2046]   162.997940   108.070198   126.246201   316.257294   150.017334
## [2051]    63.165699    84.410851   171.117508   103.228973   184.181961
## [2056]   128.134460    96.351128   153.885193   137.993439   146.109863
## [2061]    55.486549   125.815994    74.643288   147.303848   209.121445
## [2066]    81.348869   138.495544    78.516380   138.899933   105.118408
## [2071]   256.828674   279.139435    71.244278   228.747498   112.244621
## [2076]   283.406708   147.858551   140.016693   100.916626   125.007950
## [2081]   292.258514   278.151642   143.912643   108.032700   108.535538
## [2086]   123.837746   116.700867   238.449173   155.430054   105.810295
## [2091]   165.993866   281.593628   232.270432   110.088608    91.897171
## [2096]   158.958511   147.643188   103.893539   139.255219   113.384315
## [2101]   126.984116   213.673904   170.239197    85.942070   122.733002
## [2106]   102.657784   251.346359   123.444542   118.229958    72.907974
## [2111]   128.323547   113.339691   103.196259   109.709450   171.197403
## [2116]   124.942993   137.995941   105.786613   118.212181   148.905334
## [2121]   124.760994    73.690575   116.196350   327.708923   179.887009
## [2126]    88.248283    82.817131   116.947708   315.333374   162.986237
## [2131]   119.654228   132.059265   114.155785   112.629166    92.471481
## [2136]   220.177505   191.877686    60.312283    78.799004    90.413193
## [2141]   114.488815   103.667786    36.240917    90.480690    78.669792
## [2146]   292.218018   200.711823   221.217072   201.059113    96.483818
## [2151]    91.561104   155.796478   108.658386   108.437027    98.207039
## [2156]   119.237297   172.424332    66.292198   172.751770   282.699738
## [2161]   147.467819   116.623009   334.231995   101.492592   133.259949
## [2166]   244.259415   124.558434   231.003250   111.362289   115.507416
## [2171]   194.192764   364.636108   187.790359   139.696686   118.808777
## [2176]   176.567154   165.924362   390.580078   125.434814    93.851715
## [2181]   168.147705   210.974960    77.707840   133.668457   104.296913
## [2186]   106.751923   151.348267   129.706680    89.817474   202.416077
## [2191]   129.043365   235.467728   148.982391   102.855003   195.897964
## [2196]   260.558685   191.814301   138.430603    85.668823    61.956127
## [2201]   132.404816    79.069611   102.059235   115.448273    90.864845
## [2206]   189.150879   201.615005   121.420784   109.785637    90.712616
## [2211]    99.206207   273.273132   137.146500    99.158981    93.606529
## [2216]   101.330978   118.822014   134.056595   163.617538   141.714447
## [2221]   141.154007    69.205971   131.192047    31.014652   154.233978
## [2226]    62.576096   111.740768   146.716965    84.542900   138.840668
## [2231]    99.464256    58.804222    43.015179    36.262211    64.302147
## [2236]    87.698532    99.514069   291.302643   131.508133    84.584099
## [2241]   107.785492   164.945465    92.672455   138.595306    98.471817
## [2246]   284.216553    95.737640   183.758575   232.720627   103.058388
## [2251]    56.449799   116.444771    96.927864   185.686829   109.241592
## [2256]   303.857330   299.954224   110.109703   270.912109    66.355591
## [2261]   125.290802   181.182266   152.557083   118.120361   103.594551
## [2266]    92.628609    70.344254   100.802292    86.594727   126.110840
## [2271]    58.312599    77.184982    59.763603   159.057724   147.370590
## [2276]    89.577133   126.542442   178.070496    85.673790   220.014099
## [2281]   196.252533    82.051857   149.017838   115.141289   116.722633
## [2286]   142.484055   100.396797    70.972588    99.305145   129.529373
## [2291]    82.057213   185.376083    81.038658    79.183128   130.900406
## [2296]   110.827797   303.985168   140.372665    85.325607    96.709984
## [2301]    85.171898   151.502808    96.252785   156.798508   199.883743
## [2306]   316.477905    35.666481   656.172668   129.124893    85.326187
## [2311]    81.950264   134.271652   269.134460   277.843781   227.827759
## [2316]    72.145889    84.852272   163.091232   127.847374   305.606995
## [2321]   360.376434   145.294403   125.399925   118.414551   142.203262
## [2326]    89.166389   281.005035   121.008743   102.371994    75.246475
## [2331]   145.324112   208.966721    90.470001    60.053059   233.407928
## [2336]   128.494003   135.533356   142.136444   325.604980   105.040260
## [2341]    93.131386    92.603500   154.251434    75.992012   138.465576
## [2346]   147.399017    92.453079   117.048172   105.150513   196.957397
## [2351]    99.274612    77.547707   108.990616   109.493240   106.365578
## [2356]   156.259659   100.762810    76.749214   251.804520   104.001465
## [2361]   171.070892    68.763344   123.484856    76.919601    85.959175
## [2366]   173.130936   277.056488   105.780907    90.226868   152.404373
## [2371]   135.752731   230.490112   167.414978   137.627304    63.785519
## [2376]   269.350250   145.152756   217.439224   169.399323    77.872322
## [2381]   117.006165    60.149937   183.648193    95.736641   134.049667
## [2386]   194.680084   220.936600   244.893402   126.280724   258.710968
## [2391]   193.287521   183.596191   133.437546    72.033203   200.914185
## [2396]   149.113708   137.115204   202.665176   118.504883   140.644348
## [2401]    80.780785   210.920212   178.994354    83.548233   295.769379
## [2406]   106.295540   110.774094   685.056824   211.846375   114.980270
## [2411]    82.465561   104.572197   143.503799   126.431412    87.271660
## [2416]   244.034592   129.069183    73.470741   111.622864   275.993164
## [2421]   141.644409   274.833771    58.540264    71.094612   122.566109
## [2426]    68.170311   686.721008   156.546646    95.191757   121.479454
## [2431]    74.513817    92.251564   137.298508   179.082062   183.814301
## [2436]    72.109489   119.495239   119.510338   133.445541   220.648651
## [2441]   107.057602   241.471222   313.870667   358.792542   296.732147
## [2446]    73.803436   143.446426    74.883415   175.849197   133.484314
## [2451]    51.540531   114.427261   166.363068   375.641022    96.611221
## [2456]    79.196342   110.514915   104.063240   152.881958   108.632675
## [2461]    86.315117    47.869419    92.707001    96.171257   105.334198
## [2466]   114.064339   337.079468    99.162476   210.245636   157.666672
## [2471]   163.323669   152.627274   104.803741    67.436546   283.853882
## [2476]   182.855911   150.560562   248.217819   131.176422   124.102936
## [2481]   101.713959    90.571396   110.790237   104.229332   109.341393
## [2486]    74.522881   111.983215   198.618881   104.225235   230.537415
## [2491]    86.761917   135.491318   204.577942    92.611923   116.438904
## [2496]   188.929352    79.541885   257.301636    91.245270    85.422188
## [2501]   226.710922    97.496330   112.407127   360.940552   223.929611
## [2506]    50.919193    92.362686   119.350670    -6.759485   231.317047
## [2511]   167.316467    73.790359   105.168961    82.537323   134.756027
## [2516]   132.616135   165.330719   121.524879   110.853020    96.863792
## [2521]   302.680908   115.336044   183.546997   154.149994    84.190521
## [2526]    96.039925   149.431412   158.047195   104.285828   337.191376
## [2531]    85.291237   233.874146   128.692627   108.980148    72.270424
## [2536]   166.756424    95.105652   177.371964   117.347847   139.509842
## [2541]   413.264740   140.433731   120.482010    91.545395    90.989479
## [2546]   259.884064   238.661758   111.247192   120.377441   159.201248
## [2551]    76.984161    74.313057   157.329178   179.781921   135.975128
## [2556]   119.990860    92.819298   187.482407   125.415466    96.746048
## [2561]   172.930466   125.194908   151.766388    91.830971   162.839706
## [2566]   110.504974   125.016609   153.402039   255.122604   176.853775
## [2571]   229.355133   170.875168   148.502838   124.252754   344.305908
## [2576]   159.069458   138.644699   152.031326   128.860260   287.562866
## [2581]    55.318420   217.399658   134.036362    97.348488   183.007187
## [2586]   129.617020   202.687103    71.296341   257.516266   125.796844
## [2591]    67.938095   188.607605   124.567688    76.775925    94.127327
## [2596]    83.632072   105.430138    97.919983   105.473351   197.610611
## [2601]    94.722542   184.091400    90.686584   137.495422    99.214516
## [2606]   111.205315   134.481079   149.391373   158.290466   130.015900
## [2611]   163.124084   108.474632   117.554855    81.232651   200.222488
## [2616]   103.771271    83.089119   102.387306    92.448250   163.478989
## [2621]   106.571419   221.613846   122.360054    92.388878   137.638596
## [2626]    99.702011    91.794464   147.166489   137.973480   160.894302
## [2631]    40.145988    92.626137    79.267044   124.673683   128.959106
## [2636]   200.004166   204.684601   188.807266   137.904282   138.257050
## [2641]   104.461220    78.673225   122.218292    92.036926   134.084991
## [2646]   121.937149   134.127762   140.225266    80.295761    71.197845
## [2651]    66.910103    87.082138   159.067749   128.921661   134.808945
## [2656]   179.061859   113.806908   105.293640   233.467239   100.846329
## [2661]   100.617432   198.450912   121.653603   131.536438   132.916428
## [2666]    91.903137   149.630402   161.717590   191.733841   115.869545
## [2671]    93.756767    99.120720   196.670914    68.969803   208.650757
## [2676]   297.003845   302.543884   130.757156   162.259827    27.933643
## [2681]    78.388802   130.925491   152.364777    93.196159     5.311580
## [2686]   235.601227    -7.752636    64.592422   121.919922   111.495743
## [2691]    47.060402    96.500839   361.030060   131.263245    90.697838
## [2696]    53.550236    82.467323   168.632294   219.293289    83.978561
## [2701]    73.595406    32.165936   296.468445   241.761185    85.490257
## [2706]   101.496048   227.516647    40.951103   138.407486   123.836105
## [2711]   134.214142   141.460220   218.973404    42.266075   112.785645
## [2716]   108.382835   178.799026    66.362282   148.917236   147.547165
## [2721]   118.811356   177.404007   116.058228   107.910721   352.303619
## [2726]    99.261711   130.458694   196.514786   240.275253   110.022758
## [2731]   124.722229   210.380707   124.886559    97.599579    84.709656
## [2736]   114.054413   186.335876   335.288513   131.833099   160.178314
## [2741]   192.065521   136.246399    98.410378   136.544907   105.307228
## [2746]   121.301071   139.674179    77.208076   149.245438    57.585564
## [2751]   114.483971   156.689255   145.588470   180.420013    56.821400
## [2756]   209.116013   283.041107   139.813004    83.891174    77.974434
## [2761]    68.499031    72.193802   133.066849   144.328049   120.249062
## [2766]    81.518845   113.058624   133.849915   203.442078   134.950363
## [2771]    77.563400   245.403854   138.368515   209.008667   131.739410
## [2776]    79.318695   236.721909   270.106201   117.987709   129.354355
## [2781]   115.417030   104.422379   119.595490   103.820152   139.932724
## [2786]   115.677826   140.348389    85.547600   205.684448   142.904663
## [2791]   143.941879   102.623840   114.806534   135.255814   110.302689
## [2796]   107.219315   164.483765   111.914528    96.275436   117.494385
## [2801]   111.774704   156.760818   138.150330   101.593712   160.538712
## [2806]   113.806282   135.739868   139.304214   166.546341   129.762054
## [2811]   165.041763   116.120102   135.372192   155.055008   266.609314
## [2816]   100.064934   143.594345   243.773148   162.271500   239.998077
## [2821]   152.969086   234.624146   136.181534   148.331070    79.240471
## [2826]   147.035507   225.786453   158.853149    70.107864   118.135483
## [2831]   206.275665   238.995255   125.033493    77.114975    71.856728
## [2836]   104.452728   113.426712    93.171188   134.787109   168.110336
## [2841]    69.405991   175.879013   118.644798   137.956757   158.274170
## [2846]   129.221222   120.387695    83.571854    80.721474   127.856941
## [2851]    96.868500   187.257187   155.310486   110.610306   110.685486
## [2856]   219.905914   157.472870   139.634598   114.828690   108.882935
## [2861]   123.753342    64.840706   127.587708   117.003227   143.781250
## [2866]   131.295441   121.454773   270.200745   164.165100   119.792778
## [2871]    51.198959   101.618996   122.375031   241.264069   109.459869
## [2876]   124.266914   178.881393   263.464020    82.346169    85.442856
## [2881]   152.506409   141.573318   175.991608   244.984955    98.935387
## [2886]   151.734238    95.961510    77.868553   139.842468   109.936707
## [2891]    92.076347   124.872589   119.466873   126.776024   143.621765
## [2896]   134.534958   262.389374   135.147202   148.882584   147.945831
## [2901]   100.855408   125.177429   114.710609   161.597382   118.218124
## [2906]   180.947266    98.278732   203.880508   139.561844    56.029934
## [2911]   115.654388   101.220016   122.975143   210.200897    86.384972
## [2916]   230.591751    56.410759   136.361130   102.056709    85.970802
## [2921]   148.347290   159.526474    27.806816   147.740021   138.929901
## [2926]   126.566818   161.580002   247.306931    55.763058    48.038017
## [2931]   131.144363   142.430984    78.876106   117.604385   179.635590
## [2936]   116.597397   116.856613    87.834389   105.605202   123.896088
## [2941]    57.842144   142.189835   147.974106   188.078812   224.771393
## [2946]   103.564224    94.453094   168.594727   198.016663   118.376007
## [2951]   123.881004   304.319458    48.136734    98.068108    91.988632
## [2956]   104.026382   127.617226   112.628967   127.770065    76.640167
## [2961]   111.877197   237.859299    53.923069    -4.439453   131.464127
## [2966]   108.396866    70.588913    59.192944    73.203896   321.023743
## [2971]   118.065834   169.999817   173.028870   182.608917   195.799820
## [2976]   135.908554   171.049408   133.686737   114.988075   152.622208
## [2981]   136.362854   194.230774   124.216675   111.564987    77.627930
## [2986]   159.380341   221.607590   156.712738    85.218987   365.060577
## [2991]    77.981071    86.985451   165.993469    86.783943   115.597244
## [2996]    94.601028    63.923573   126.818031   207.024170   175.920670
## [3001]    92.450882    97.365860   132.100784    94.704933   134.077972
## [3006]   179.837326    45.549168   107.054840   144.870316    72.865265
## [3011]   220.293701   126.887665    81.179688    67.474495    86.434952
## [3016]   229.836151   116.055702   149.099197   144.421036   119.145706
## [3021]   115.728493   124.271393    82.657509   102.751129    98.303391
## [3026]   172.850769    84.712273   157.286087   145.558487   129.149445
## [3031]   125.551834   106.700287   137.999329   111.386887   146.558426
## [3036]   138.878571   111.115120    86.577988   142.189514    61.392281
## [3041]    75.690201   173.231384   126.714035   139.826248   118.324577
## [3046]   138.171692   158.780716    97.651878    92.226318   211.072525
## [3051]   151.846878   156.925125   151.466568   120.403038    92.736526
## [3056]   260.426727   145.716583   111.431725   123.725166    92.187668
## [3061]    95.404953   187.020706   180.800629   273.843933   100.025711
## [3066]   205.486862   134.082275   156.419220   193.575256   141.692551
## [3071]   141.435608   135.011795   111.517288    92.831009   182.419586
## [3076]    71.197052   186.739166   239.757339   107.361000   101.230980
## [3081]   100.497665    83.983627   102.570290   128.674927   200.500183
## [3086]   203.959534   201.331772   142.646591   104.992073   110.339996
## [3091]   372.969574   131.628708   167.221603   161.771011   118.319695
## [3096]   117.795944   153.476593    76.548492   240.169937   174.228638
## [3101]   131.612518   152.294647   114.619743    90.641014   159.775360
## [3106]   109.547867   138.113602    66.913925   116.302742    92.852455
## [3111]   138.396530   102.239288   131.544617    90.350853   139.206070
## [3116]    96.662682    82.691002   137.050003   103.822823   128.509277
## [3121]    96.632729   292.299164   292.257446   157.626099    92.492920
## [3126]   199.461655   107.241158    95.645073   159.848206    94.159775
## [3131]   233.860153   141.638779   104.460670   221.425156    65.947151
## [3136]    82.717804    99.021492   129.161285   180.607986   141.757202
## [3141]   140.399597    73.281868   133.380966   167.942154   110.788567
## [3146]   110.968307   114.576370   138.405029   172.776352   165.604614
## [3151]    87.816032   140.488983    99.581528    88.436752   138.540253
## [3156]   147.147995   173.682312   135.329239   164.063660   163.372192
## [3161]   255.581070    84.354027   146.055801   140.572922   102.589699
## [3166]    93.325233   205.134903    67.087563   136.519379   106.525238
## [3171]   179.845490   259.357483   123.086014   168.030853   240.934845
## [3176]    79.767899   101.424301   165.199173    84.455215   264.867249
## [3181]   177.572281   184.929901   103.118332   183.141235   187.591324
## [3186]   134.617615   136.819382   117.836189    98.529579   159.224442
## [3191]    91.204277   164.410660   186.460022   222.461853   185.673569
## [3196]   113.401230   216.675079   230.012756   131.768066   110.048172
## [3201]    85.129349   141.622955   139.543762   166.745712   114.222549
## [3206]   135.642273   135.761597   143.114334   139.307877   119.216064
## [3211]   223.808182   192.251144   126.516914    89.772575   109.495415
## [3216]    99.964241   215.730804   122.591888   123.624748   173.325989
## [3221]   104.700684    59.113369   198.510071   128.073776    86.614510
## [3226]    86.209488    64.645058   124.591202    88.609390   160.647095
## [3231]   159.274841    94.120659   190.617050   125.239624    81.176872
## [3236]   185.738693    80.529549   184.398926   100.983139   156.041412
## [3241]   119.328514   103.759415   119.444359   129.019058   115.698654
## [3246]   182.748810   136.218063   168.169662   149.452744    36.097263
## [3251]   232.205353   116.814743    96.522644   114.646805   112.236992
## [3256]   108.291405    93.889618   102.369804   267.941284   109.215904
## [3261]   183.704819   148.756409    96.183617   101.033867   115.242554
## [3266]   145.368927   145.370438   248.341034   188.284241   142.410446
## [3271]    79.124832   144.581573   180.112106   144.972107   143.588242
## [3276]   102.131744   186.087921   138.881241    93.880608   145.558258
## [3281]    61.499699   246.354858   178.109100   156.934158   167.837341
## [3286]   102.928223   121.934753   101.288055    86.943047   112.327995
## [3291]   126.980591   152.208267   102.514900   113.002960   277.935211
## [3296]   118.250748   199.312164   237.164688   184.172195    60.513035
## [3301]   126.621216   187.929749   182.310028    91.448227   240.130783
## [3306]   127.096436    80.277397   145.400848    61.085487   176.831924
## [3311]   137.506241   187.991943   133.276672   113.401611   118.308487
## [3316]   127.470673    89.678474   164.216660   210.214355   123.778412
## [3321]   175.494797   151.444946   115.472107    95.568756   101.727676
## [3326]   127.396355   117.554459    69.194633   165.157364   115.846260
## [3331]   121.215630   122.521706   157.260925   134.890625    95.569763
## [3336]    95.242264   111.399681   171.757278   133.359253   199.902908
## [3341]   152.947113    82.705978   194.171173   173.401108   221.390381
## [3346]   130.788208   132.007736   157.364365   115.248421   127.868698
## [3351]   137.676987   114.873131   145.239273    96.552567    71.868538
## [3356]    83.447968    85.674492   121.601486   131.276306   209.722916
## [3361]   148.677261   131.280075   120.626152   292.808624   117.254745
## [3366]   104.629059   165.626999    97.529243   124.486923   111.990891
## [3371]   126.568039   242.028687   158.473145   184.374115   145.734619
## [3376]   125.810852   115.848419    99.948753   119.929520    67.085480
## [3381]   240.155792    73.694839   196.673721   108.864632   103.532745
## [3386]   172.234848   102.533661   104.046631   112.281754    96.496735
## [3391]   136.032486   276.310547   107.511490   133.432220    81.982857
## [3396]   171.349045   134.520630   142.644226   122.331924   111.256134
## [3401]   254.102905    81.647667   124.362747   118.777374   161.443588
## [3406]    93.401497   120.323212   138.087738   121.576973    80.405411
## [3411]   186.547836   203.922043    68.668236   147.336258   100.504410
## [3416]   102.955452   120.035919   185.092117   137.834122   224.151520
## [3421]   280.157440    69.344475   200.796356   153.524139   100.972946
## [3426]   107.724037   100.209442   176.714462    91.799728    83.334808
## [3431]   144.457474   198.315399   149.281143   138.802109   227.980835
## [3436]   148.133347   118.570648   175.985687   131.784988    83.860832
## [3441]   171.042877   121.517136   137.273956   213.687515   219.672928
## [3446]   207.334473   235.167511   142.150970    63.449539    78.329590
## [3451]   106.095268   168.218292    98.254364   122.552238    76.194725
## [3456]   184.228104   150.841431   119.750870    89.545792   184.856293
## [3461]   203.313782   174.049759   129.750732   134.001450   118.919540
## [3466]   302.212402   122.489685   121.286484    97.632195   170.736282
## [3471]   102.723808    91.470161   124.009453    85.153778   234.576706
## [3476]   171.053238   144.838989   118.144409   106.041862   133.068176
## [3481]   170.686630   155.128601   107.301437   127.766769   220.625534
## [3486]   109.407738   130.244186   136.282990   105.014244   143.080399
## [3491]    59.109707   125.806389   101.376541   105.227844   124.613518
## [3496]   142.404053    87.464638   179.053528   231.537170    86.091782
## [3501]   120.472321   305.244629   144.282990    97.627213    90.683380
## [3506]    81.520905   182.597382    98.938873   292.401123    96.831093
## [3511]   160.155487    90.656364   238.581665   122.698929    84.021797
## [3516]   149.219345    34.414391   328.837067    11.656833   -66.501045
## [3521]    74.386528   191.119598   -45.689293   141.023178    86.406723
## [3526]   177.574356    88.568489    95.580009   132.262817    25.000751
## [3531]   141.290863   103.282074    78.492935   159.918411   129.184387
## [3536]    49.643730   154.467285    96.879295   114.042747    78.476295
## [3541]    86.316444    82.505783    93.189377   184.805801   188.488586
## [3546]    61.995827    88.927284   118.118286    84.503967    75.141670
## [3551]   113.612328   118.529678   247.848938   122.883545    85.887146
## [3556]   111.610786    73.954430    93.926224    84.533249   207.591187
## [3561]   104.392204   107.919937   134.207657   299.488556   286.782837
## [3566]   328.090790   204.362991   302.290924   211.084229   105.126709
## [3571]   144.007034   309.095062    88.986137   439.499817   283.084839
## [3576]   133.791794   137.486877   110.883392   305.440216   238.360977
## [3581]   303.386871    98.181068    93.670242   114.110535   179.696457
## [3586]   150.312805   137.462082   103.174423   185.263504   110.535614
## [3591]   220.242386   295.322449   135.259872   299.580994   136.711319
## [3596]   301.243042   122.400574   298.141266   161.106461   283.050629
## [3601]   280.517883    99.463127   145.712173   293.542694   298.754944
## [3606]   244.335663   294.148651    85.818558   196.222687   306.430298
## [3611]   226.435974   441.657196   122.678062   148.626114   275.512085
## [3616]   181.420364   428.816681   176.600433   146.006653   312.404175
## [3621]   318.593384   410.917572   311.278412   126.165482   436.629120
## [3626]   116.992073   126.807327   135.081100   120.052620   296.549164
## [3631]   263.020355   299.498444   171.938293   238.942062   297.198975
## [3636]   245.609009   258.474091    92.033760   141.638535   302.339874
## [3641]   112.070915   123.836891   441.573151   229.390244   107.137817
## [3646]   133.229752   289.148346   113.629898    89.735298   293.627289
## [3651]   303.540314   134.500641   254.256012   115.996368   157.950607
## [3656]   297.417297   152.219772   294.334503   260.795166    79.214836
## [3661]   294.035065   133.667542   134.848618   160.146194   112.925385
## [3666]   214.228043   170.269928   293.705902   235.549835   293.198395
## [3671]   145.426804   182.679077   156.809799    98.055977   108.471062
## [3676]   325.897186   200.494720   127.192955   309.242310   167.715088
## [3681]   230.794556   111.736458   140.465286   167.427048   261.576691
## [3686]   203.868896   133.257294   245.324905   237.717667   161.953094
## [3691]   250.379608   229.428741    89.359589    86.149940   147.985733
## [3696]   112.049599    58.424442   130.540375   151.144623   177.074738
## [3701]   118.414230    88.716942   174.669128   140.089798   176.060974
## [3706]   131.708954   126.630722   120.449837   122.022644    97.433693
## [3711]    86.364433   295.703522   165.950272   111.577034   130.750473
## [3716]   165.292511    95.731155   124.215019    67.755112   105.283745
## [3721]    97.718475   111.949394   171.921005   196.894150   114.093689
## [3726]   120.037804   136.483536   121.767159   175.043976   174.104767
## [3731]   141.673279   100.599236   152.793808    76.932129   491.258270
## [3736]   189.736984    93.825455   160.737610   119.227798    98.522194
## [3741]   190.264069   139.702774   110.564644   333.285889    83.998817
## [3746]   148.486404    79.915306   356.844604   171.560455   113.762871
## [3751]    95.247337   157.757797    89.490181   168.056000   143.559250
## [3756]    78.236710    58.629715    89.758308    86.167900   104.688545
## [3761]    71.851562    75.178757   415.105255    63.740269   209.220795
## [3766]   206.489426    58.756050   194.551834   101.666565    90.737625
## [3771]   112.044556    84.389267   151.648270   210.935791   121.249657
## [3776]    59.847755   146.626785    90.676918   109.103622    58.929504
## [3781]   124.520912   115.396706   152.292557   147.382217   118.511353
## [3786]   392.805756   143.528732   170.105682   130.639862   126.911835
## [3791]   130.586655   148.671585   333.932007   297.584106    84.043892
## [3796]   152.617264   147.112701   181.241882   108.550598   174.248138
## [3801]   118.671600    73.071579   215.210861    58.852428   104.486282
## [3806]   110.936890   170.899933   309.408234   161.370667    81.132301
## [3811]   178.207687   130.431198   248.350311   125.730141   160.351807
## [3816]   274.404205   326.344208   174.192520    56.932922   255.520691
## [3821]   213.209671    82.404266   110.739677   105.537506   157.763336
## [3826]   123.834023   114.850548   287.670807   110.563972    96.227921
## [3831]   106.616776   153.801346   163.742874   130.107574    80.449677
## [3836]    69.847961    80.160110    40.904613    81.788963   113.724503
## [3841]   117.397049    89.247437    76.043312   127.248985    82.451164
## [3846]    99.272415    60.868649    70.975395   104.867210   200.264603
## [3851]   227.813843    99.202728   112.824089   103.790482   123.172943
## [3856]    81.707947   110.889900   131.540466   113.025101   140.485687
## [3861]    60.163975    93.401176   109.964745   106.689201   212.162903
## [3866]    84.826614   150.485397    53.789402   110.674103   229.494965
## [3871]    87.015160   281.331055    79.035202   152.432388    86.584229
## [3876]   276.160339    85.710709   187.741272   155.455612   115.249924
## [3881]    95.566978   153.912537   227.751419   107.087891    80.856689
## [3886]    99.305969   208.488449   662.804321   733.791260    73.803162
## [3891]   122.583961   101.004448    72.120140    25.649782    73.990509
## [3896]    96.023476   147.204697    77.099060    84.199348   110.551659
## [3901]   102.711494   185.386047   181.868347   117.476646   110.062119
## [3906]   113.219193   670.834229   158.237610   176.417892   116.883736
## [3911]   134.143112   166.133087   148.776215   137.934082    73.946503
## [3916]   136.380859   154.064224   125.258911   120.946724   117.458870
## [3921]   103.518814   100.664574   116.272774   116.511200   213.445587
## [3926]   137.687546   151.701355   175.827118   216.379120   125.700050
## [3931]   190.096252    77.265503   107.889481   107.115005    89.380524
## [3936]   172.264709   180.222977   127.174637   116.264511   155.938522
## [3941]    66.979774   142.367447   109.493172   128.358047   118.425858
## [3946]   444.198364   170.424683   118.161613   133.069885   155.596451
## [3951]    97.932243   278.813995   132.433197   137.785599   286.847198
## [3956]    84.883255   144.694199    90.624512   117.029984   197.695435
## [3961]   241.039627   135.833633   135.500214   130.104919   224.597290
## [3966]   110.062576   125.047493   132.679535    93.262146   192.299759
## [3971]   152.592285    83.039299   161.699539   186.798203   176.270401
## [3976]   123.652176   165.636505    96.319366   135.948547    93.762054
## [3981]   124.902336   208.432205   181.388748   107.228432   199.103134
## [3986]   112.354706   128.653503   163.667984   113.140808   143.626846
## [3991]   100.330383   140.284958   209.442474   205.452789   196.410538
## [3996]   186.030624    94.223778   200.595383   178.970779   303.162903
## [4001]   135.461060   183.384872    65.745209   123.062042   125.775856
## [4006]   281.588257   112.672531   154.346542   115.901825   212.862335
## [4011]   267.171722    75.051422   148.451385   215.054062   111.622108
## [4016]   265.174866    94.653542   104.000473   107.808289   130.493301
## [4021]   109.852722   140.095993   126.968643   101.789001    92.340050
## [4026]   254.564743   197.196671   103.990219   175.205627   171.925888
## [4031]   149.029434   152.498215   227.061752   170.450165   317.023621
## [4036]   126.845482   121.765305   354.076324   147.005463   190.979614
## [4041]   172.881119   179.159454    87.719666   174.835480   155.484619
## [4046]    77.094566   160.270584   113.599716    89.766800   133.498230
## [4051]   136.943176   253.088638   106.305519   154.789810   168.607452
## [4056]   188.522079   172.183121   194.743713   196.020004    78.381500
## [4061]    95.924507   220.114670   142.961746   162.358063   327.359924
## [4066]   210.981628    81.138466    63.486835    69.403297    96.501839
## [4071]   303.014954   237.126678   112.207336   235.635254   232.384537
## [4076]   141.244263   116.997925   331.239899    39.596008   127.787155
## [4081]    84.938965   133.640930   100.096130   264.346893   166.702103
## [4086]    73.506088   111.258850   207.875381   172.094269   152.840988
## [4091]   159.742874   138.184586   131.310150    99.805122   185.972656
## [4096]   158.906174   105.534767   235.479156    94.216438    65.671722
## [4101]    89.845818   190.424454   209.041306   178.399246   464.692352
## [4106]   207.878189   112.901665   199.122620    66.801575   113.660049
## [4111]   166.960373    83.029381   216.689255   153.237595   152.791443
## [4116]   104.279099   143.994049    85.655769    33.570251   196.988800
## [4121]   258.121979    79.893234   502.744690    48.396030   193.206940
## [4126]   184.627960   136.250916   175.506546   284.548676   342.594269
## [4131]   119.151100   102.037109   159.414047   195.502106    95.058754
## [4136]   105.800331   213.323868    87.764069   151.065781   163.688843
## [4141]   146.472549   109.626343    80.989693   194.614624    84.408813
## [4146]    67.477058    97.576355   380.273285   168.936798   149.138077
## [4151]   168.088928    99.125374   132.302277   133.716110    88.894279
## [4156]   198.263794    85.653893   153.306076   167.975204    81.706200
## [4161]   124.255676   158.168945   152.975143    72.287529   238.215714
## [4166]   114.553123   108.751389   119.881729   162.800720   256.019440
## [4171]   192.214600   125.192963   154.312515   127.475945   144.437668
## [4176]   197.353760   119.108093    86.447266   181.444336   115.626541
## [4181]   120.319855   126.847305   119.303711   199.258026   155.291687
## [4186]   100.073380   142.119385   133.342743   132.915314   133.059158
## [4191]    91.042435   163.530502   -27.697807    75.251610   144.754700
## [4196]    16.548416   118.572403   141.806671   261.765228   158.289322
## [4201]   165.546448   195.857819   157.616089   108.542244   163.307724
## [4206]   277.529144   117.822113    88.865913    68.681969   123.831741
## [4211]   104.124054   147.184952   185.383392   101.732834   178.178940
## [4216]   163.879120    98.072182   217.713028    83.702713    56.580265
## [4221]    82.166298   128.905212    85.107681   181.255081   311.970306
## [4226]   183.461624    97.787819    75.705307   131.362152   116.982483
## [4231]   126.970497   144.268692   114.106331    68.794884   157.325089
## [4236]   117.431992   119.698883   206.386276   176.716782   139.562988
## [4241]   190.025558   112.404388   185.247894   136.459274   174.326431
## [4246]   158.751907   132.415482   149.937607    92.217964   135.707214
## [4251]   156.908310   220.349960   179.010834   217.418839   113.876678
## [4256]   114.355537   157.021454    87.125824   124.822426    87.419106
## [4261]   197.469757   162.608292   100.126938   131.229767   152.489014
## [4266]   247.679932   108.106514   134.050400   170.563599    83.661621
## [4271]   141.250244   190.777328   111.775215   147.628677   100.427361
## [4276]   156.986649   119.210304   252.880692   133.379913   195.573349
## [4281]   171.172226   113.670776   135.981537   139.356369   120.094566
## [4286]   155.460281   231.218506   123.158646    89.597771    88.690010
## [4291]   129.785141   196.192291   114.474701   207.539444   101.061798
## [4296]   128.101593   157.792465   158.394196   122.546875   146.046860
## [4301]   129.235596   238.022583   218.354355    73.385880    94.878006
## [4306]    63.563442   151.574707   200.760437    85.027275   164.799332
## [4311]    64.842117   107.707260    90.875282   224.700180    69.088844
## [4316]    57.567574   124.838692   107.018723   148.546631   114.173256
## [4321]   145.110107   164.630356    96.461319    95.319756   177.668976
## [4326]    94.338570   163.920593    89.087784   120.286453   167.257462
## [4331]   101.854912   125.179520   180.314758    89.009613    85.042931
## [4336]   202.798752   125.267609   127.066544    95.860382   111.586281
## [4341]   255.905396   167.949188   144.267944   109.262497   136.008118
## [4346]   135.708054   216.759872    91.472816   105.757088   125.364861
## [4351]    71.360100    50.185635    70.938408   147.157608    94.495872
## [4356]   106.646255   100.770744    94.700119   223.029007   123.281662
## [4361]   119.855415   177.493164    50.360573   131.579926   121.812370
## [4366]   161.100601   186.150818   329.608582   147.479324    68.794662
## [4371]   127.101227   157.513611   152.652481    78.814186   181.123947
## [4376]   108.603477   143.441269   187.600388   135.187332   136.969482
## [4381]   122.444901    89.481316   196.953217   122.233810   120.125961
## [4386]   133.195450   113.352707    80.876030   148.718933   144.992615
## [4391]    59.969147    76.631866   140.588531   125.004799    94.884186
## [4396]   211.873306   120.175697   172.714523   164.943588   139.805054
## [4401]   275.575928   146.081329   132.768539   141.724289   159.755478
## [4406]   115.358994   132.949829   152.448105   258.418549   180.647934
## [4411]   340.502991   125.673317   138.147949   119.496223   100.608574
## [4416]   118.765869   125.150833   271.881653   189.537109   203.719543
## [4421]   129.735077   181.067215    74.102959    93.822037   112.657860
## [4426]    72.376617    82.963013   109.366966   133.266632    98.471733
## [4431]   137.681473   221.699860   108.832542   157.704361    85.656837
## [4436]   202.482803   142.926224   158.147415    95.769951   145.519241
## [4441]   171.069305   144.533539   119.712173   139.530716   162.267319
## [4446]    99.523506   135.059021    93.034332   117.814865   136.420212
## [4451]   526.765198   151.575760   139.561234   171.974792   145.062775
## [4456]    86.106331   196.199585   175.748398   204.885345   120.592628
## [4461]   115.338974   112.352470   234.722610   116.674179   161.487518
## [4466]   347.325745    94.873955    83.964546   311.643250    71.369530
## [4471]   205.727036    99.350327   187.319885   173.410995    88.664185
## [4476]   192.383331    95.901665   176.844696   151.123459   -75.647354
## [4481]    74.585007   175.488968   120.610672    80.856148   122.859917
## [4486]    74.670166   146.852997   163.685074   161.203049   171.451767
## [4491]   334.572327   112.670074   212.964828    73.942223   109.636681
## [4496]   120.847954    85.596420   172.921158   257.954895   108.254745
## [4501]   120.509109    78.290901   118.983963   176.992737    87.415276
## [4506]    79.325882   260.871368   130.465302   114.839813   107.796051
## [4511]   131.578796    97.480499    92.339783   181.696381   155.999619
## [4516]    42.911236   174.489090   149.231369   127.626541   408.753845
## [4521]    88.658005   194.125778   139.456909   209.641235   105.299232
## [4526]   168.352478   118.810181   110.647141    74.169312   325.869354
## [4531]    86.327888   128.807922   148.838562    86.364594   207.846024
## [4536]    90.530190   135.579773   101.092712    50.634007   144.719025
## [4541]   158.073425   191.487671   123.704849    78.248543   242.581299
## [4546]   192.056381   211.996246    80.258156   116.513237   105.143616
## [4551]    94.543724   117.251152    91.296989   492.298370    98.382538
## [4556]    93.137573    85.378639   145.400238   147.320679   127.080200
## [4561]   199.666473   146.668671   157.517517   153.067886    99.136452
## [4566]   113.485756    77.779762    31.136150    43.080017   148.377975
## [4571]   208.378479   228.157822   116.939072   135.332336    84.627251
## [4576]    72.512802    77.083778    76.541214   129.435135    73.674881
## [4581]   129.994812    99.285400   286.620056   132.674057   181.402939
## [4586]   105.322662    97.745232   139.316940   121.379784   142.052750
## [4591]   233.666290    82.821213    82.596344    92.870010   195.930908
## [4596]   273.239044   145.103088   310.476105   125.635353    76.255409
## [4601]   213.488037    94.763588   111.094627   131.865173    86.805275
## [4606]   152.514359   102.511833   116.351654    81.829788   195.070694
## [4611]   189.363541    98.558098   193.118454   123.035904    93.730492
## [4616]   185.290436   157.236176    74.563065   146.620789   239.480957
## [4621]    96.545097   304.552216    69.748627   107.423462   140.133865
## [4626]   127.971771   151.903961   130.006668   149.323334   100.983742
## [4631]   102.521622    78.108467   154.366882    89.868523   122.362419
## [4636]    95.737129    54.647163   123.342560   147.871536    95.508583
## [4641]   103.794060   142.348602    74.756042   112.836723   187.514771
## [4646]   132.083786   112.999069   128.132401   206.063965   179.113556
## [4651]   237.837936   178.512665    70.431618   141.529373    83.833176
## [4656]   201.819351    90.280197   112.545914   148.718842    85.781013
## [4661]   106.070724   181.653290    97.898682   134.733215    58.611618
## [4666]   115.629448   159.206680   289.573517   108.829010    84.747375
## [4671]   502.572449   -47.310905   106.673874   189.608185    80.641418
## [4676]   299.838074    95.855423   156.542145   153.915878   106.875168
## [4681]   103.287971   182.985718   166.486755    85.009270    99.003296
## [4686]   116.904610   139.129318    94.912277   159.625229   103.842827
## [4691]   102.263153   172.071198   132.262299    72.096581    85.710732
## [4696]    41.369705   128.046722   124.481895   119.957565   189.995743
## [4701]   232.212357   126.610565    79.023201   117.634216   100.581497
## [4706]   137.785843   128.363678   319.216095   196.332245   330.115692
## [4711]   211.145676    89.631477    95.143257    97.049568    90.657127
## [4716]   231.256531   121.656647   164.113586   142.184769   100.006760
## [4721]   203.734039   112.754135   107.538803    71.521660    94.519157
## [4726]   109.030891   163.486893    77.454819   106.580460   105.180893
## [4731]   106.537239   147.850220    72.979309    74.151283   231.519669
## [4736]   169.342361   107.859848    99.573158    86.545753   124.869537
## [4741]   118.585258   192.248810   223.990234   232.876175    84.475182
## [4746]   104.136459   117.258575   105.238670    97.428505   156.152893
## [4751]   104.881767    96.945564   111.165421   122.270714   119.721443
## [4756]   115.448158   127.677536   124.383438   120.459625    90.334869
## [4761]    92.343269   120.967346   144.222458    67.553474   146.180115
## [4766]    88.845917   111.785316    84.948517    53.750729    83.286942
## [4771]    56.719391    79.843033    99.738930   114.496735   113.845108
## [4776]   105.815865   109.658340   148.231674   245.994064    79.241882
## [4781]   152.241623   191.358383   107.806229   178.451370   145.804520
## [4786]   174.666656   123.055946   306.212738   138.651077    99.843018
## [4791]   145.998489   153.358047    68.538628    82.911652    97.511520
## [4796]    88.962471   195.511658   160.888290   141.725006   107.831734
## [4801]   175.210037    98.031479    60.527588    91.140930   164.852585
## [4806]   175.648254    78.779121    37.473850   127.069405   123.760704
## [4811]   111.520721   110.474197   131.303696   363.254822   125.388512
## [4816]   156.173767   174.504089    79.470879   107.160896    84.366875
## [4821]    99.703438   209.117508    98.082901   119.267685    94.741791
## [4826]   114.052834   105.874779    64.247421   129.573608    77.404778
## [4831]   100.184624   101.953316   160.648590    93.005432   118.901550
## [4836]   145.379166    89.772194    99.049973    74.408234    68.365585
## [4841]    81.289146   101.352890    74.612000   133.759354   193.914673
## [4846]    83.236038   146.108109   137.457642    61.139866   128.502960
## [4851]    97.256317   235.184998   128.153442   137.161606   129.020233
## [4856]   401.608032    71.055092   107.067535   149.860428   115.322418
## [4861]   127.554626   250.430984   121.678055   126.876213    90.266548
## [4866]   104.575294    68.605118   146.002899   133.535065    86.526909
## [4871]   143.870010   136.748993   114.056610   116.470482   135.958237
## [4876]    97.926201   114.291389   134.677475   144.130920   124.170746
## [4881]   199.261856   135.669769   181.401474   273.379944   114.601868
## [4886]   120.410072   184.383820   158.806610    86.989899   100.437195
## [4891]   -52.254517    95.651421   141.028595    68.614204   209.519806
## [4896]   190.355957    72.227547   125.424545    84.674957   109.678864
## [4901]   136.292557   123.184967   146.184830   189.228119   148.162674
## [4906]    36.233063   141.118286   110.527428   103.026505   127.550636
## [4911]    88.818108   172.522888   147.365509   241.299255    75.851395
## [4916]   105.529968    93.129196    85.180717   132.516296    88.383347
## [4921]   103.590416    87.924522   161.975189    98.718681    75.537231
## [4926]   109.502197   116.026718   102.269341    77.833755   118.184082
## [4931]   132.327652    96.958565   159.455002   122.951591   123.436066
## [4936]   103.226357   162.067139   114.040810    95.839325   112.833115
## [4941]   146.760239    92.349396   257.442627   112.064972   165.146225
## [4946]    81.595604   143.438126    89.752708   106.513077    95.155144
## [4951]   129.707626   100.326012    90.218613   143.252319   132.538025
## [4956]   157.614929    70.780846   157.300385   131.110840   321.478729
## [4961]   337.385010   140.497620   101.979889    92.626747   201.004852
## [4966]   103.002686   152.012192    42.809822   145.068390   108.839989
## [4971]    94.895645   103.308609   142.658081   117.878082    57.097446
## [4976]    61.850567   105.238197   247.985367   144.208298   101.442101
## [4981]   145.455612   111.824921   227.516556   293.648804   216.783646
## [4986]   142.498810    89.872124   213.259766    99.620941    81.816483
## [4991]   177.533127   190.328369   192.253098   169.672165   129.543274
## [4996]   128.490295   109.339600   103.115555    79.228127   104.827194
## [5001]   125.264786    90.887695   353.455231   142.838547   143.088242
## [5006]   144.523941   188.243973    95.947510   109.320457   172.720093
## [5011]   285.791199   114.709885    92.017593    82.235222   114.069916
## [5016]   129.822098   125.333099    61.458740   187.371414   165.201599
## [5021]    28.323483   109.030869   151.618958   232.180359   191.831467
## [5026]   266.052277   134.530807   371.327179   146.560455    78.464310
## [5031]   123.023476   106.688026   184.109009    96.747711   211.530228
## [5036]   154.411926    83.066635   209.294815   137.104111   154.975143
## [5041]   226.953049   135.280518   137.646103    56.370438   142.083405
## [5046]   223.255493   184.403381   187.502625    47.406513    98.158485
## [5051]   179.505493   194.022507   109.130119    87.359871   127.000397
## [5056]   274.866821   153.289825   149.528671   134.271301   140.189911
## [5061]   108.045914    21.532314    73.093277   139.538589   189.233566
## [5066]    93.626190    52.194378   287.179138    79.081215    92.153694
## [5071]    93.156380   232.295609   175.525726   107.963463   155.723694
## [5076]   109.946465    71.781006    94.973251   207.952194    36.066109
## [5081]   172.229630   199.561783   114.938599   142.400406   164.363495
## [5086]   111.682159    98.298401   170.228638    82.339996    95.713860
## [5091]   225.217575    93.044090   159.240295   103.734344   315.616302
## [5096]   252.534302   126.791000   131.082504   294.565155    91.134491
## [5101]   159.464294   101.519936   174.608398   315.124207    80.727158
## [5106]   122.535965   139.298615   149.295456   128.867752    82.444778
## [5111]   194.727722   112.628174    88.857315    79.630669    98.139282
## [5116]    82.255486   109.082733   198.075150   145.882202   147.766724
## [5121]   105.627640   202.300705   101.388321    67.803558    69.061508
## [5126]   114.313797    78.953094    94.500793   256.457703   156.488388
## [5131]   224.557877   219.776428    65.877945   100.001785   111.981613
## [5136]    95.009842   151.943314   364.790253   364.970093   183.807281
## [5141]   158.303864   109.214096   189.944244    90.007576   315.988281
## [5146]   144.492630    84.713890   108.976257   190.231598   112.076981
## [5151]   129.385544    85.150063   170.314728    96.826813   165.080307
## [5156]    82.072815    94.143829   134.281952   134.488693   163.536057
## [5161]   251.242966   293.400330    29.975395   122.655594   106.504166
## [5166]   252.923859   202.206223   149.006927   275.459442    90.503143
## [5171]   128.235764   107.144630   115.531944    80.659752   153.813293
## [5176]   177.983383   104.979797    74.276314   186.631561   106.404022
## [5181]    78.391800    92.475433    99.372940   278.208618   161.710739
## [5186]   227.883087    72.163681   195.143173   132.945068   186.010315
## [5191]   122.971161   130.631882   260.327148    48.887375   251.515686
## [5196]   178.229858   114.088196   175.059097   140.564484   144.749832
## [5201]   118.022385   124.549843   180.152969   146.840363    58.804409
## [5206]   180.047058   103.911530   134.470810   139.655624   126.096939
## [5211]   129.017532   194.547653   103.960243   104.466408   149.685059
## [5216]    29.972740   145.119888   111.748466   232.771194   117.164162
## [5221]   224.710617    27.202560    91.405396   148.535660    90.503387
## [5226]   283.357697   148.718079   124.968483    94.452042   214.837448
## [5231]   114.789093    94.913651   103.776199    74.076820    84.740036
## [5236]    55.916145   171.245270   278.687744   133.380325   141.099335
## [5241]    95.965919   157.343384    57.998756   203.360443    41.444519
## [5246]   133.938004   107.789909   107.502365   126.802673   118.299957
## [5251]   213.620499   139.400192    10.812244   172.952896   141.331177
## [5256]   211.604706   156.373550   115.606102   110.652679   205.594879
## [5261]    68.772324    61.483932    61.353516    78.561676    75.724625
## [5266]   167.850372   115.395996   155.092392   165.671692   106.677353
## [5271]    60.218525   153.028793   138.630478   108.292244   170.264023
## [5276]    95.066887    33.820984    83.041969   169.451828   205.161102
## [5281]   134.349930   176.938446   169.029022   200.325684    50.006828
## [5286]    71.000801   225.839706    90.579315   133.837784    49.297150
## [5291]    95.713676   277.432312   100.718773   254.455780   162.765213
## [5296]   181.606995   173.390778    91.524773   149.002502   129.102554
## [5301]   196.765991   101.057999   203.157578   134.612671   120.763573
## [5306]   165.862869   157.897858   104.498413   186.895828   138.743347
## [5311]   150.697998   167.774445   136.786591   119.867592    71.496277
## [5316]   120.557175   145.985977   309.295349   226.793808    76.794594
## [5321]   152.833694   184.297989    73.278709   152.206711   218.101776
## [5326]   117.628609   196.320312   101.803482   159.293213   148.046005
## [5331]   133.070953   185.319382   321.972321   142.239685   110.164841
## [5336]   129.760208   118.731125   144.891800   102.745628    34.463814
## [5341]   235.021164   124.525391   118.371880    93.012672    91.253716
## [5346]    97.750237   127.686768    98.279533   325.531464   299.618469
## [5351]   147.349625   225.305008   159.078842   138.570297    92.581871
## [5356]    88.326797    78.817802   142.999481    91.188683   139.612061
## [5361]   110.218033   171.148819    78.826843   195.144623    96.442322
## [5366]    86.312744    69.896942   128.465073   125.555496   127.247017
## [5371]   160.063263    86.924957    56.597599   154.040802   172.480316
## [5376]   211.248627   106.059799   140.365189    73.307594   131.344833
## [5381]    65.128487   247.570969   136.588745     1.643243   292.680786
## [5386]   129.292419   111.492859    91.839645   219.107468    99.668976
## [5391]   149.764496    51.964520   120.887039   143.111923   131.522736
## [5396]   115.600822    72.609116   111.949425    93.662277   218.087540
## [5401]   279.509796   107.650970   111.889359    69.287918   138.230194
## [5406]   158.889008   184.005508   236.598328    83.716393   356.678558
## [5411]   321.735748   150.223465    98.764587    85.054970   100.155693
## [5416]    87.884544   101.818634   115.807243    54.302517    82.581848
## [5421]   136.212616   106.922440   138.246490   103.962616   418.373474
## [5426]    84.598198    87.898895   188.380325    92.190903   215.205429
## [5431]   106.541306   115.750595    81.504036    99.521988   204.492752
## [5436]   113.132996   158.482422   126.564064   270.776031   163.769806
## [5441]   138.982498   162.988663   231.002792   151.109497    84.239662
## [5446]   192.395477    63.062809    78.746155   120.262421   100.138359
## [5451]   131.577225   263.785522    81.249397   127.165085    80.309441
## [5456]   132.260757   123.195358   107.461929    76.914528    66.157639
## [5461]    69.886818   201.456329   120.483063   139.817413   121.880699
## [5466]   145.925018   134.235596   131.396866    90.996330   135.245438
## [5471]    83.095268   127.846657   299.743103   128.400208   129.882751
## [5476]   165.804504    64.137299    76.134888   155.560349   107.933144
## [5481]   117.749626   103.721962    94.410713   141.556808   104.278328
## [5486]   125.416840    86.688255    83.895065   124.070541    86.513504
## [5491]   161.361374   105.743736   124.627823   100.516960   123.341782
## [5496]   181.917908    87.961212   120.165291    51.038620   229.857895
## [5501]    88.557846    28.524067   159.901184    83.223061   107.788269
## [5506]    23.096928   205.194229   197.590225   114.191452   100.703728
## [5511]   165.389664    84.554703    74.391083   133.925598    57.194370
## [5516]    72.999458   186.429779   107.339035   200.770065   158.912384
## [5521]    83.176826    74.873314   194.178223   194.925674    25.200619
## [5526]   101.808121   116.871704   150.882996   206.981369   190.310883
## [5531]   123.397102   123.848816   124.176048   234.668045   507.951019
## [5536]   298.816254    94.800430   187.363480   117.111855   348.630005
## [5541]   152.826263   114.143158   263.768768    94.557594    51.817688
## [5546]   260.590057   125.969162   106.592133   117.021362   123.126007
## [5551]    84.368965    89.682693    17.268791   168.259338   101.214775
## [5556]   180.220413    82.114326   134.033844   115.033730    87.864670
## [5561]   131.114792   152.399231   119.329231   105.490082    51.690331
## [5566]   157.113983   119.615456   138.608398   169.382645    92.562531
## [5571]   129.450134   153.276581   162.905563   125.963852   287.082520
## [5576]   313.941132   452.875397   137.768509   123.916916   104.094841
## [5581]   128.436005   293.116486    70.620613   119.178246    69.441788
## [5586]   170.978928   118.688324   309.287354   163.886444    78.789185
## [5591]   246.678101   171.626587   178.780548    70.886887   225.468765
## [5596]    90.678391   118.228867   138.983795   101.070007   102.119064
## [5601]   109.748596   189.613037    94.713402   136.629364   120.005646
## [5606]   106.314873    93.982185   120.885605    77.921532    98.714462
## [5611]   121.271446   119.480232   135.759155   110.812866    73.083199
## [5616]    61.489544   125.685677   170.905014    81.095490   172.268158
## [5621]   303.284058    86.786438   177.402679   127.853027   122.241707
## [5626]    94.595734   177.215347   105.214920    68.901413   133.811996
## [5631]   119.499191   176.967743    90.002655   127.842873   279.286621
## [5636]   161.397903    89.119583    81.033813   118.629761   104.464203
## [5641]   102.027382   151.852493   120.216476   128.361008   112.174492
## [5646]   119.702988   249.989639   262.001343   109.443497   130.805771
## [5651]   247.226044   117.114822   114.662827   120.419220    88.068771
## [5656]   141.264572   126.976547   140.833572   167.639786   117.441376
## [5661]   184.575745   152.811554   188.159912   174.624542   139.033310
## [5666]   236.109802   391.423096   167.112030   188.059311   212.988983
## [5671]   123.248184   150.304398   246.490936   128.025955   105.381104
## [5676]    86.080673   185.596542   170.123489   108.090515   153.677246
## [5681]   172.987137   119.751076   158.681335    75.857727   226.291611
## [5686]   128.142502    98.837173   271.060455   204.212738    90.727287
## [5691]   116.354614   181.269897   121.714882   197.392853   502.349182
## [5696]   112.443649   198.476181   131.548325   211.944778   107.889633
## [5701]   136.146255   222.425293   112.247192   118.156250    84.865265
## [5706]   214.785965   430.123322    86.836449   276.285736   139.526276
## [5711]   276.173523   158.455475   204.613159   142.173721    80.173340
## [5716]   205.490601   249.775986   236.250320   118.865875   206.789459
## [5721]    89.753510   271.190613    98.012032   133.493347   284.007172
## [5726]   201.435318   198.884323   229.354370   104.949997   118.019897
## [5731]   196.280685   166.750320   187.526703   285.159088   222.628632
## [5736]   126.290695   184.812439    63.496552   187.715790   127.424637
## [5741]   190.740707   223.937943   182.918839   130.301865    90.118973
## [5746]    91.764549   127.978821   373.453644   236.233932   557.499207
## [5751]   122.089523   183.045578   258.706757   109.611526   106.810226
## [5756]   122.989403   138.683167   127.231842   176.996338   147.848724
## [5761]   103.546555   128.269073   157.467499   317.495117   112.746727
## [5766]   154.220795    95.744087    82.722511   144.211533   172.316666
## [5771]   145.201065   101.558632   177.259567   174.060760   111.639816
## [5776]   103.506332   137.580063    98.701447   108.518005   161.547623
## [5781]   187.394104   147.687714   230.281769   368.227020   225.918045
## [5786]   118.190666    70.659645    50.885178   117.646729   127.076469
## [5791]   114.569099   148.042786   240.159729    75.268555    92.505020
## [5796]    51.394875   110.600853    93.386284   342.303833   132.069519
## [5801]   142.792816   120.586304   129.570328   259.354370   304.969635
## [5806]    87.463440   234.824280    89.620773   363.098328    61.012878
## [5811]   128.425659    94.736687   168.505981   153.920853   499.394196
## [5816]   175.180618   138.314468   109.378708   185.660767    88.136551
## [5821]   119.017517   110.171875   261.063446   145.908020    69.210533
## [5826]   193.160507   152.303925   136.356033    61.419815    90.001404
## [5831]    75.212967   125.389587   144.855270   122.780945    65.710358
## [5836]   218.864441   116.708023    55.646267   133.757385   267.803925
## [5841]   118.834930    88.779182   229.903992    46.375763   -26.976536
## [5846]   107.844505    80.187294   157.969711    61.181686   103.937630
## [5851]    89.118507    52.950935   135.866119   132.992233   138.925308
## [5856]   156.516022   391.574768   153.904465   170.604813   104.677834
## [5861]    74.613831   203.613510   180.774063   134.849014   116.264175
## [5866]    95.750069   125.848122   115.052200   105.685623   104.354828
## [5871]    99.717812    42.345436    91.757988   104.270821   129.285385
## [5876]   152.118134    92.573227   226.426117   124.736435   130.810547
## [5881]   113.566711   107.763908   153.127304   104.333092   110.643753
## [5886]   176.497009   131.926407    71.533066   130.960220   178.983109
## [5891]   176.322968   104.634651   185.953583    69.143837   184.460220
## [5896]   102.271111   218.834702    99.309341   170.433167   113.727287
## [5901]   244.624237   115.664566   141.539871   117.998802   149.336655
## [5906]   111.512260    69.479561   140.329376   169.604416   156.983612
## [5911]    80.999245   228.290497   100.907127   206.876419   233.420761
## [5916]   120.853294   121.632248    81.652840   107.560989   135.439041
## [5921]   137.774017   117.716148   180.862122   176.045914   191.047241
## [5926]   133.072723   115.835297    91.866196    93.664177   218.004425
## [5931]    82.653061   123.394226   152.491394   212.807144    80.511002
## [5936]   123.514313   102.092079   118.190605   177.925308   312.604492
## [5941]    59.773605   175.898926   139.444077   153.402344   123.231560
## [5946]   164.077881    79.822746    69.498581   185.181793   148.594543
## [5951]   148.150284   217.020126    52.626907   126.418472   166.601974
## [5956]    89.134605   124.491798   119.270210   132.817749   213.352936
## [5961]   142.226059   106.905327    96.816696   106.307037   118.892776
## [5966]   200.670609   109.316055    86.390076   116.745758    92.183578
## [5971]   188.719742   105.154991   133.789322   125.504158    94.043503
## [5976]   109.836472   122.864128   137.674973   219.795227   162.318146
## [5981]   131.968964   173.711563   163.765320   153.186096   141.596420
## [5986]    66.498299   123.100677    70.934746   126.690796   144.159851
## [5991]   105.238602   188.793182   105.071373   116.158852    97.992371
## [5996]   117.217323    72.044685    80.191719   131.220520   100.970001
## [6001]   111.450165   179.322418   168.766739    42.837837   110.294731
## [6006]   -46.092648   195.800537    95.011322    64.971191   210.454697
## [6011]   113.989868   149.470825    84.890381   216.595627   104.875244
## [6016]    85.633034   103.067696   234.260712   159.422195   125.819336
## [6021]   140.732346   -57.279068    65.752457   117.046417    83.570457
## [6026]   101.152527   120.467117   100.661232   -52.048534    65.868187
## [6031]   137.667923   107.543762    92.605934    86.810196   211.153793
## [6036]   103.640343    81.152451   -50.966610   175.391464   134.144775
## [6041]   211.070236   151.440048   207.589813   147.571518    39.006832
## [6046]   130.236603   188.101257    93.630836   105.040131   171.534393
## [6051]    77.884346   107.986610    73.938484    86.020882    91.836784
## [6056]   127.524139   107.047440   288.564911    80.260666   150.286148
## [6061]   120.747070   105.990883   119.412987   161.286407   109.961060
## [6066]    63.758717   133.983963    70.889801    99.312256   177.555756
## [6071]   167.882675   100.238930   157.321976   129.746384   137.984192
## [6076]    82.877380    87.030350   180.061539   108.106934   115.011330
## [6081]   100.362732   131.343887   144.908936    71.278503   134.785217
## [6086]   139.453323    61.217762   102.781013   121.494919   216.315063
## [6091]    86.870712    74.821999   169.874359   153.402023    86.114700
## [6096]   117.059029   220.776428   112.818794    73.893837    56.254822
## [6101]   177.639633   135.254623   290.983795   221.170700   119.856277
## [6106]   253.602570    76.689224   109.488167   115.074463   154.737778
## [6111]   147.895020   158.314606   101.392784   220.933655   167.868744
## [6116]    89.618790   245.189011    70.192207    93.197540   159.488586
## [6121]    94.401604   140.415268   189.298019   139.208221   469.591248
## [6126]   190.947449   258.364471   161.280289   269.181335   117.211914
## [6131]   204.335861   190.640228   158.223541   103.366791   154.313919
## [6136]   166.732697   132.216217   112.908157    96.882103   121.416466
## [6141]   212.281647   276.347168   246.526535   245.682358   238.243607
## [6146]   142.544128    54.362480   253.127701   142.343063   150.908142
## [6151]   170.951233    90.365669   233.938995   142.181931   117.011208
## [6156]   138.595367   165.695160   114.064201   202.137161   105.135345
## [6161]   295.378937   194.763153   189.025314   217.537933   166.820084
## [6166]   156.956345   211.890717   169.711517   177.248108   212.252304
## [6171]   142.292755   144.985687   164.755737   152.519928    90.636833
## [6176]   140.842636   254.154297   105.825851    88.225449   196.916901
## [6181]   163.910248   131.501160    51.395672   315.123871   366.938202
## [6186]   238.036179   118.663780   175.383194   134.095200   663.469482
## [6191]   287.992035   222.951401   151.374817   177.977478    79.562271
## [6196]   121.789673   221.727509   165.717163    85.626244   173.270676
## [6201]   185.630325   159.214615   237.484695   255.634827   122.036728
## [6206]   273.223816   105.436310   211.258240   116.268967   147.351181
## [6211]   150.445251   139.047379   251.046066   162.492249   147.124420
## [6216]   111.284225   146.478775   105.607315    90.910263    89.004143
## [6221]   135.831482   117.689011    71.929543   163.747833   177.289749
## [6226]   217.378525   172.624603   154.019363    31.249817   257.987061
## [6231]   315.131226   113.805511   288.218628   104.702095   117.559845
## [6236]    96.605476    57.659180   187.360321   146.053268   144.368500
## [6241]   144.436340   248.785416   155.414841   233.428467   140.338989
## [6246]   158.564362   287.839539   175.608154   154.483398   250.234055
## [6251]   161.821579   184.874359   314.319397   218.420029   314.319489
## [6256]   213.589340   130.290390   149.737289   150.427994   151.869141
## [6261]   105.214279   105.380577   147.416214   264.842590   156.699036
## [6266]   173.498169   246.958328   112.005821   133.790359   183.051788
## [6271]   372.243164   231.629501   168.289581   183.404572   151.253906
## [6276]   112.854706   254.896591   114.936020   196.400925   152.075439
## [6281]   161.537292    62.208908   131.772858   240.228745   314.171661
## [6286]   113.342865   127.833519   156.442535   143.709579   127.975250
## [6291]    50.887447   116.929436   117.078392   188.720062   178.336807
## [6296]   100.040443   284.838501   126.516106   105.671158   157.254883
## [6301]   137.771683   487.660889   141.387955   152.343445   127.247169
## [6306]   214.381683   114.498657    34.220150   271.734436   107.225327
## [6311]   170.566269   193.583450   161.196198   176.156326   122.713699
## [6316]   213.584564   118.155838   100.600258    56.023033   134.852448
## [6321]    92.328087   210.170685   133.941910   100.476143   163.101288
## [6326]   178.863327   170.916428   149.766464   163.593719   180.441101
## [6331]   181.067108   120.192154   128.268478   150.040680   228.143784
## [6336]   166.234772   255.827393   134.284698   136.576035   166.377579
## [6341]   150.978699   197.209183   148.775864   186.538162    44.770771
## [6346]   134.663727   173.609329   118.886124    82.515884   164.390167
## [6351]   226.798248   167.710495   245.682205   178.981964    74.308861
## [6356]   195.828552   307.357819   165.330231   252.157761   296.951538
## [6361]   138.105637   201.025986   153.932510   176.986832   171.007690
## [6366]   177.801147   123.893860   167.677994   188.407272   128.102234
## [6371]    93.579048   196.132980   237.026581   148.375107   165.695282
## [6376]   241.302185   170.752609   196.071793   113.374527   508.824585
## [6381]   125.889832   152.939178   139.149109   162.474182    94.368729
## [6386]   126.585312   173.005753   142.765198   242.059158   233.844131
## [6391]   128.563538   207.811813   124.548607   242.329880   165.722046
## [6396]   178.483536   207.289749   241.083649   250.232529   184.972717
## [6401]   180.698975   232.434769    93.161285    60.599079   213.589355
## [6406]   115.981094   123.123566   191.817139   166.174240   243.938522
## [6411]   199.209885   160.269821   154.644348   111.011955   100.301971
## [6416]   134.376953   119.070053   170.375061   143.782089   148.352386
## [6421]   129.807144   157.365494    85.444199   135.231140   187.440796
## [6426]   174.223831   151.815140   143.549683   162.491348   134.757599
## [6431]   164.789520   135.234268   166.024460   108.507751   124.831627
## [6436]   157.624557    91.736145   141.962799   166.585785   195.048431
## [6441]   215.585876   129.342972    89.755424   104.653633   190.595337
## [6446]   129.046463   114.913483    75.251953   139.490280   267.668121
## [6451]   132.268829   103.519257    95.728088    52.233929   159.503387
## [6456]   144.874084    99.732544   128.010910   113.899498   125.674744
## [6461]   125.948540    81.299561    63.488277   145.600189   202.049820
## [6466]    82.317337   138.439606    73.570152   102.090363    76.944824
## [6471]   151.042786    72.063927    98.038620   104.711929   147.101898
## [6476]   121.396126    70.783325   213.425949    84.097580    84.329079
## [6481]    61.165989   154.360260   148.394028   102.603973   153.045349
## [6486]   116.918976   105.632591    70.175201   182.236450    88.177849
## [6491]    77.997978   140.990082   112.185562   144.960052   114.404434
## [6496]   105.559784   112.865021   177.848373   104.428383    65.750549
## [6501]    95.300896    98.428062    91.682190    82.209496    68.773132
## [6506]   108.648033    92.042786    99.324890   136.555862    68.895004
## [6511]   164.413147    89.534172    81.315582   129.448441   113.998756
## [6516]    72.494217   115.261597    81.305634    39.536411    99.636047
## [6521]    88.948410    90.243042   119.923744    85.111153   190.934875
## [6526]   245.092285   133.443466   115.498291    72.111900    59.259098
## [6531]   148.492264   166.601349   163.076889    89.025429    62.087536
## [6536]   142.021866   131.077164    92.366554   115.104141   100.287674
## [6541]    81.884171    69.243202   171.644745   116.401421    49.607521
## [6546]   118.918068   165.562424   167.420975    86.700912    89.836304
## [6551]    99.270920   178.979050    77.646416   155.459518   120.718964
## [6556]   169.625610   296.606293   103.822067    76.586617   114.913902
## [6561]   128.035675   220.999756   260.065094   223.546219   158.067215
## [6566]   298.152893   209.796158   164.716782    81.472694   280.629883
## [6571]   154.155685   164.246628   161.601608   181.071075   181.401779
## [6576]   127.089462   158.297363   161.348251   132.007462   119.339035
## [6581]   159.225021    80.923111   240.830414    73.107376   122.653458
## [6586]   213.879059   229.135132   228.213959   204.537628    99.879417
## [6591]   127.841110   224.012619   124.150070    85.632347   130.540527
## [6596]   153.088715   247.921906   165.148132   141.149155   143.544968
## [6601]   174.721848   102.364807   190.683243   192.664810   196.357681
## [6606]   135.466934    39.985062    70.085716   123.477524   189.880112
## [6611]   133.223373   299.375336   111.339783   196.155930   224.849274
## [6616]   225.542450   211.785431   229.218857   145.821686   210.206985
## [6621]   173.126633   134.009018   221.852310   117.201591   262.020416
## [6626]   188.258972   125.634689   204.706406   111.212257   229.163559
## [6631]   186.384430   238.903015   165.747238    93.501907   125.924149
## [6636]   172.852936   288.489471   183.560043   112.817947   203.205826
## [6641]   230.901794   149.601288   234.770248   166.498383    96.368790
## [6646]   148.591797   183.469040   142.302719   140.636612   132.851959
## [6651]   105.432030   122.769142   132.468369   280.535461    93.732582
## [6656]   185.109756   199.805573   155.570358   178.497635   222.813431
## [6661]   110.395386   175.721695   177.770935   111.945595    99.618202
## [6666]    55.487000   109.950882   139.478043   123.414680    94.550797
## [6671]   100.169884    76.502510   172.330872    94.146339   283.776855
## [6676]   124.971748   214.357727   175.820160   162.327759   128.559097
## [6681]   162.702621   121.980148   269.606720    83.666267   176.099976
## [6686]   135.851089   119.271423   135.608383   133.684158   217.361435
## [6691]   119.433411   153.140457   138.692352    60.191952   136.236465
## [6696]   143.917496   143.987793   300.252472   146.166336   159.483582
## [6701]   100.812469   130.979553    38.632679   117.227097    58.489620
## [6706]   127.267151   111.366707   132.699234    77.426048    96.362968
## [6711]   157.499374   112.353958    71.007645   144.869400   149.038284
## [6716]    95.402748   213.584946   122.804276   158.184143   128.489426
## [6721]   182.951340   118.739059   105.578064   146.449478   162.814423
## [6726]   112.347290    82.363846    83.324318   258.235260   142.372864
## [6731]   107.038193   134.423920   139.810913   102.193840   259.436615
## [6736]   322.372681   182.996826    72.931404    77.297401   174.897751
## [6741]   104.275635   132.201660   238.927765    90.467796   142.182953
## [6746]   163.860855   230.929138   202.575348   169.668594   152.931046
## [6751]   182.586349    92.199318   176.676025   208.330124   137.662247
## [6756]   208.918396   156.578354    90.786980   205.318024    79.333000
## [6761]   359.966125   138.159988   391.184326   161.957718   149.511841
## [6766]   160.240891   282.889252   285.220825   124.531845   164.865738
## [6771]   128.194687   138.028030   240.837784   242.640381   135.722382
## [6776]   203.645264   133.069443   142.445740   156.527466   117.579803
## [6781]   129.706223   125.305664   125.406860   131.118484   117.262344
## [6786]   319.412811   114.448677   113.376610   257.923523   101.729095
## [6791]   169.623474   114.399544   149.210236   205.958450   213.856827
## [6796]   223.196823   163.633606   163.838043   115.627098   125.483032
## [6801]    95.682823   185.182846   141.490173    84.064560   105.047966
## [6806]    83.206772    83.042938   111.485428   252.308594   131.136230
## [6811]   143.103958   247.940903   180.768936    94.437645   116.702377
## [6816]   115.953156   116.548073   207.407318   142.468170   114.274567
## [6821]   193.269684   240.065552   185.685638   138.000305    67.668144
## [6826]   139.029907   183.954514   141.682373   260.511719    90.420334
## [6831]   230.058731   105.935654   328.184967   126.823380   132.902985
## [6836]   234.006256   122.356003   262.400909   312.849426    79.833115
## [6841]   110.419052    80.049347   161.083847   100.493889    65.176636
## [6846]   194.152466    80.168076   131.955170   107.761154   181.166306
## [6851]   172.144424    77.831902   355.645264   125.003220    83.267105
## [6856]    57.404884   101.340431    70.542488   184.686310   277.843079
## [6861]   140.458084   104.469994   127.596199    66.832680   131.293411
## [6866]    99.249870    95.997627    84.210632   172.323502   158.825073
## [6871]   227.261292   254.524033   222.502548   122.602425    80.569489
## [6876]    97.882767   185.875015   116.344223   154.257782   155.427002
## [6881]   357.891602    70.994484   121.184525   102.081581   217.611450
## [6886]    92.998779   158.471786   102.693550    88.930069    80.297653
## [6891]   137.346832    94.569466   113.253647   106.274925   256.431763
## [6896]   207.840073    97.946213   127.176025   251.317490   184.703842
## [6901]   275.623230   145.340637   132.070023    86.338463   120.562698
## [6906]   116.759270   128.872742   200.869644   128.392822   126.772118
## [6911]   117.296181   113.925049   206.296341   122.925827   106.458412
## [6916]   172.218552   141.320404   137.411835   179.263046   123.106056
## [6921]   263.413177   189.139771   334.504456   101.823135   155.599792
## [6926]   256.839569   122.551704   129.377777   150.700165   156.364197
## [6931]    93.581596   114.944672   124.850006    92.458946   100.454514
## [6936]    20.255562   117.467049   176.752823   175.567413   129.737534
## [6941]   208.868851   138.988480   166.720123    65.493660   213.391876
## [6946]   121.906555   178.601715   138.089294   111.715294   106.743446
## [6951]   102.039230   122.880249   138.254425   147.392029   246.537201
## [6956]   182.071335   243.265259   157.116470   140.973846    85.069710
## [6961]   101.465126    94.724342   123.063698    98.186623   138.753113
## [6966]    85.056145   127.986954   112.586754    -5.389462    73.294357
## [6971]    71.626747   307.930267   183.026245   200.351257   132.219650
## [6976]   120.233978   145.714890   138.559067   155.222397   123.647705
## [6981]    94.316505   154.050690    97.478012   282.639587    86.159325
## [6986]    44.633450   111.184265   196.144882   120.684387    39.301018
## [6991]    97.974861   241.676743   233.138260    94.098145    89.354408
## [6996]    68.911606   142.138062   214.830978   216.019012   172.023849
## [7001]    65.012268   121.589134   194.895691   153.981537    81.999825
## [7006]    86.979958   187.231445    76.878387   106.361832   166.161530
## [7011]   194.258148   144.586349   209.279068   137.477371   163.787933
## [7016]   113.291611   121.112885    91.229050   110.158844    72.200989
## [7021]    66.415825   104.838791    85.588951    62.835682   117.925514
## [7026]   220.115509    86.070656   145.482346   108.414978    79.575287
## [7031]   182.706497   114.789673   213.739044   228.311798   180.118469
## [7036]   153.600357   108.065323    84.630836   131.858154    80.644676
## [7041]   251.516586   249.012558   124.205009   124.029938    95.103813
## [7046]   133.949219   137.476974   159.945419   116.050980    92.002190
## [7051]   125.178459   158.632980   103.781059   105.211014   104.422791
## [7056]   192.001968   289.808746   168.748734   139.910400   101.994644
## [7061]   120.134300   208.053772    95.108765   141.803802   177.786621
## [7066]   189.056381   107.889168   134.421478    99.390251   124.680664
## [7071]   154.081512   152.062256    88.163277   145.007767   205.011276
## [7076]    96.272812   150.235275    80.440414    68.282990   160.858414
## [7081]   275.715698   101.804634   192.931610   363.228546    59.711521
## [7086]    80.862923    94.861458   115.566803   136.189636    90.353203
## [7091]    86.356537    88.274582   362.194824   100.406120   127.061264
## [7096]   153.805161   100.405167   174.472427   145.645981    99.802826
## [7101]   184.069321   131.116150    78.359688   193.644135   130.494583
## [7106]   104.603111   101.744576   102.024391   104.722031   101.143768
## [7111]   102.956451   175.970612    82.411377    68.446205   133.739578
## [7116]   130.751465   127.006653    89.197792   262.053497   107.491043
## [7121]   313.701111   159.695877   177.825394    72.022087   103.060898
## [7126]    86.527863   221.957138    92.575294   165.113739   140.540314
## [7131]   235.572021    67.531052   219.331497   270.394440   126.963257
## [7136]    95.655502    69.876923    69.182899   175.935074   103.514969
## [7141]   171.367111    89.754211    99.946472    71.858696   121.954651
## [7146]   105.637466    67.531174   109.983650    85.759506   147.315872
## [7151]   107.971519    83.550774   579.494812   102.713013    61.663593
## [7156]   187.490540   249.039154    96.559448   137.617996    76.870636
## [7161]    86.171387   137.283676    61.848438    70.793289   114.346466
## [7166]   111.595482   118.229797    91.133209   133.736923   203.604019
## [7171]   121.538185    94.746544   166.625336   136.418167    87.296257
## [7176]    91.065025   221.177750   265.828522   145.106735    79.646492
## [7181]   116.181549    89.247322   138.861710   120.449371    90.381859
## [7186]   114.431259    92.133446   279.817780   139.822052   203.569931
## [7191]    81.585754   109.520775   130.864075    99.883247   113.697975
## [7196]   102.033119    96.880066   106.413628   160.046005    90.984734
## [7201]   113.950180   137.742828   122.745621   169.209259   224.375824
## [7206]   201.763229   184.701294    66.040642   186.157944   164.861893
## [7211]   121.573891   168.583344   173.497406    97.383423   137.116745
## [7216]   124.717636   136.329590   124.502472   115.083023   166.732880
## [7221]   264.474030   134.913300   193.784988   128.281906    79.813644
## [7226]   271.372345   124.249733   240.770844   143.440903    87.874702
## [7231]    68.956703    92.382095    83.625496    97.599091   100.992065
## [7236]    97.159081   254.430252   159.937149   111.964622    84.188774
## [7241]   206.004303   191.350952   129.321854    85.615158   129.033676
## [7246]   164.121338    72.049934   127.256287   102.713409   140.247971
## [7251]   107.668770    69.064445   105.102859   124.071976   153.951126
## [7256]   115.728043    77.741043   240.009781   195.176407   234.689087
## [7261]   304.917114   112.327011   214.140533   280.802002    99.486794
## [7266]   192.721954    71.137657   156.932037   175.299576    76.239235
## [7271]   144.856934   130.579956   194.636185    82.012505   109.876160
## [7276]    37.334713   136.225769   204.100113    89.672455   130.883194
## [7281]   123.855415   166.032715   102.364868    71.582703    99.183365
## [7286]    66.656441   146.111603    93.070839   169.440125   150.016525
## [7291]   328.087799   139.597290   119.026810   200.178070   119.071388
## [7296]   135.773102   218.580460   112.022522   100.220329   266.949463
## [7301]   129.000702   371.118103   136.602570   174.532623   142.002029
## [7306]   175.997040   120.128708    58.914371   100.651611   219.077774
## [7311]   226.134201   138.076767   121.059586   192.133057    54.351360
## [7316]   191.739563    82.844872   132.766693   110.617020   121.238983
## [7321]    99.963799   107.397232   185.641510    95.141434   180.460922
## [7326]   106.474815   104.452942   131.929947   154.837662   184.608032
## [7331]   123.085121    53.284267   122.638977   220.093796   148.997620
## [7336]   131.887314   123.039474   186.766876   152.577820   126.360565
## [7341]   146.641922   155.076202   293.405579    68.676788    93.604263
## [7346]   277.981384   201.497604   137.196899    90.984535   189.044098
## [7351]   137.421707   116.415359   103.848022   362.084351   111.206200
## [7356]   149.371246    59.306305    70.984489   161.040985   218.788864
## [7361]   203.798523    66.096779   122.435295   133.677979   116.225113
## [7366]   106.390945   159.865784    93.566223   257.664154    96.493011
## [7371]    96.691628   137.382996   106.811249   154.013809   236.783203
## [7376]    83.517998   119.706665   141.146591    78.280838   128.157227
## [7381]   188.794281    75.220901   263.386475   144.832031   105.855827
## [7386]   180.434708   247.209015   181.370697   207.745361   150.766525
## [7391]   119.504669   111.037498   148.741562   101.306450   172.451584
## [7396]    56.387924   350.639435   135.204391   126.294113   161.380249
## [7401]   197.947266    87.469414    90.420074    90.968201   168.684875
## [7406]    99.375732   163.277878   235.719101    94.372482   146.068420
## [7411]   124.538139   193.039230   171.058655   162.155029   139.149811
## [7416]   123.980446   242.553360    95.459755   106.411514   134.654541
## [7421]   129.235596   112.855858   162.224548   121.225067   -62.705025
## [7426]   364.423309   128.099106   148.297455    86.690460   112.282234
## [7431]    47.176521   131.260223    96.407944   149.574600   168.500854
## [7436]   192.578979   128.404449   110.873161   132.096069   221.509842
## [7441]   135.139008   138.240509   146.985901    97.489609   110.140465
## [7446]    70.547363    98.053162   110.007042   153.112411   157.778793
## [7451]   258.063202   141.679825   134.392029   117.888741   102.788254
## [7456]   163.694855   114.426048    73.601295   142.313995   318.131622
## [7461]   110.684097   206.595169    89.801804   329.438507   128.153595
## [7466]   217.247299    94.412964    86.188454    86.770546   130.758591
## [7471]   117.191719   109.932068    71.163239   118.990433    94.884651
## [7476]    13.817130   168.820145    59.849056   117.959137   111.791283
## [7481]   150.209259   103.466591   133.909805    71.125832   166.216507
## [7486]   125.151642   310.662354    91.390045   106.673492   -47.908173
## [7491]   122.255310   -27.276272   165.942200   107.195412   109.775421
## [7496]   -23.833965   150.424805    62.537983   131.014221   -31.891361
## [7501]   101.725693    84.198395   115.847336    63.535099    77.747963
## [7506]   -31.873150   159.256592   112.478157    85.658081   -40.333614
## [7511]   111.731438    77.645569   234.070633   192.059418    61.822769
## [7516]   227.965210   113.113060   164.251419   131.914413   102.790916
## [7521]   136.702408    48.199326   111.000717   103.262466   111.640427
## [7526]    96.759872   119.383354   108.601692   133.009232   119.408241
## [7531]    90.855804   203.255768   148.789764    92.862579   123.711502
## [7536]   117.817741    74.518204    97.595276   120.422279    83.496948
## [7541]    45.406761   134.433426   106.937271   196.820526   204.925964
## [7546]    85.410576   194.514664   258.866791   137.381149   175.870667
## [7551]   229.404678   159.459244   111.332687   152.940735   302.855438
## [7556]   106.115906    97.246788   255.872726   163.520630   100.570084
## [7561]    76.259041    86.798256   127.810501   116.683624   215.739532
## [7566]   296.078125   113.131287   119.769707   137.162964    54.445015
## [7571]    81.747833   161.174805    85.128647   123.388657    93.692276
## [7576]   125.349625   262.719971    96.425133   330.342804    68.873390
## [7581]   114.238350    89.501122   185.762680   125.522743   173.597672
## [7586]   123.747292   158.594574   159.558151   167.952362    85.829369
## [7591]    83.172256   431.171936   116.714951    73.855293   246.836044
## [7596]   113.819229    79.485298    62.187477   176.853210    92.819221
## [7601]   170.759521   134.536224   171.063828   164.466705    60.955082
## [7606]   246.581863   127.575745    79.356468   107.617912   121.662590
## [7611]    84.716988   160.843262   278.531769   209.704529   271.115875
## [7616]   105.818085   120.408051   163.020020   140.652100   252.628159
## [7621]   170.338943   169.406067   110.097084   228.645721   159.360138
## [7626]    60.104824   214.035645   350.111542    83.988441   192.108139
## [7631]   204.832443   112.003754   236.949646    82.570786   277.182312
## [7636]    97.453300   265.407379    74.270721   195.069107    98.782776
## [7641]   118.623352   166.497147   184.782684   221.303802   149.654495
## [7646]   296.475281    90.275085   106.244865   175.299973   173.721649
## [7651]   119.451836   301.570312   260.701691   226.857224   104.875656
## [7656]   260.992279   196.793777   132.908432    91.908585   135.725647
## [7661]   137.483170   216.771927   140.589706   233.965714    72.235695
## [7666]   251.793396   150.988571    89.001297   107.711548   179.237030
## [7671]   173.283630   144.002594    72.784592    87.182274   146.547470
## [7676]   112.762169   218.778915   243.273712   258.888458   208.881378
## [7681]   260.020233   136.303177   422.450073   267.493164   142.012146
## [7686]   266.549286   325.871613   141.235947   175.996384   108.790222
## [7691]   334.058472   196.253586   235.836029   118.530548   239.413818
## [7696]   260.025360   201.196716   277.814697   142.193359   108.133728
## [7701]   243.514496   127.896088    86.402779   449.974854   196.336792
## [7706]   314.638763   147.880630   187.107437   259.231140   196.958115
## [7711]   261.287781   294.580109   278.204132   121.694893   196.447571
## [7716]   273.350342   240.057953   109.561684    86.919319    88.459946
## [7721]    66.390045   214.453125   144.558487    93.834442   208.761429
## [7726]   113.556206   126.621628   116.675476   110.259628   504.376099
## [7731]   100.329956    58.337811   171.808701   124.792297    78.735764
## [7736]   195.717422   125.993317   240.431671    81.973145    84.369354
## [7741]   143.800308   213.465134   136.270325   114.219322   133.776657
## [7746]   242.552505   285.220184   152.785446   230.154266   127.435944
## [7751]   149.418259   170.799179    92.811028   203.518890    86.110397
## [7756]   121.646492   108.351494   164.892181   146.000702    98.220215
## [7761]   143.433899   298.780243   111.557571   135.296188   179.819992
## [7766]    72.133301   158.176880   117.237564   141.017273   118.890808
## [7771]   227.310349   228.344101    40.460583    89.263802   152.297455
## [7776]   134.384384    85.894730   104.282135   176.399170   101.775574
## [7781]   110.841286   155.225266   114.970619   187.433029   115.235298
## [7786]   135.335083    90.737091   114.677689   336.526123    60.604668
## [7791]   157.107819   193.379959   171.721130   202.321899   166.008881
## [7796]   178.506775   136.107437   171.061920   162.700684   301.123993
## [7801]   131.419159   234.684692    96.531662    78.913147   244.404053
## [7806]   138.056198   245.567902   170.031525    88.404228    87.737862
## [7811]   133.421127   293.178223   205.363266   145.294846   144.491837
## [7816]   115.490662   137.084366    84.967232   160.995605   313.260559
## [7821]   130.550095    89.529373   150.709839    77.684036   180.718964
## [7826]   198.103897   154.941986    84.010597   156.679840   155.798630
## [7831]   113.054520   187.390106   128.510315   134.882812   123.981972
## [7836]   144.707306   154.892548   179.460648   155.948334   115.908478
## [7841]   142.274490   132.427429   144.559769   155.010483   164.434372
## [7846]   119.884552   108.250671   159.051178   109.626099   232.701981
## [7851]   124.114670   170.657104   142.398148   148.317337   174.597809
## [7856]   163.763199   123.764099   143.959167   120.166183   128.730453
## [7861]   197.029800    71.813004   172.003815   136.461426   156.257019
## [7866]   255.739777   111.732559    88.287369    94.555328   121.256256
## [7871]   157.208542   148.149353   162.145630   118.091858    81.128441
## [7876]   105.296555   164.325607    97.757065   154.568985   182.276505
## [7881]   175.702835    94.168068   124.780457    93.586143    90.026367
## [7886]   132.142578   251.217880   102.867371   143.650757   142.038666
## [7891]   151.285309   151.212708   120.307907   118.396255   129.595627
## [7896]   118.265564   374.230438   188.903030   149.585831   143.413513
## [7901]   177.596207   209.041809   122.888412    77.640106   145.890366
## [7906]   144.389587   258.004364    89.363373   123.942398   190.299133
## [7911]   168.334442   158.477875   146.520935   131.541458   104.204605
## [7916]   142.358597   145.291870   131.855316   140.020874    97.469299
## [7921]   119.069504   191.005508    81.299088   159.985458   146.163513
## [7926]   122.684761   141.851974   159.572479   136.619217   177.410797
## [7931]   117.618729   131.650803   169.856628   241.707809    96.129318
## [7936]   111.350327    87.870964   108.910347   145.227707   187.260513
## [7941]   139.693192   136.506165   170.348816   214.017288   215.145752
## [7946]   114.669525   173.240021   108.479347    93.206650   110.136276
## [7951]   120.097023   134.095551   203.189651    86.649460   130.053680
## [7956]   136.961746    34.908531   128.857468    72.962524    34.251427
## [7961]    76.398216   165.554535   197.918167   131.982010   114.347275
## [7966]   132.732391   113.295830   133.107559   182.934952   140.896027
## [7971]   112.680252   264.506927    84.221268   285.052734    58.136864
## [7976]   107.591461   204.243591   174.410583   218.046555   167.496872
## [7981]   112.997238   105.717880   132.707733    53.506329    91.835403
## [7986]   118.669426   154.445358   131.675735   112.746857   119.140564
## [7991]   134.004517   201.562592    65.935921   158.352921   198.185028
## [7996]   224.050400   127.688332   209.758865    46.281025   138.238800
## [8001]    97.406227   153.616165   131.918060   248.583984   125.248108
## [8006]   130.411667   171.562668   237.277573    65.323868   158.539459
## [8011]   181.941177    71.781906   259.944122   167.658859   109.454422
## [8016]   227.620773   145.059265   210.303604    91.674423   202.094452
## [8021]   209.538559   204.544357   147.761749   176.636292   163.396561
## [8026]   232.321671   163.424408   232.908447   195.792465   217.337997
## [8031]   142.864716   119.444046   116.188553    89.436562   117.990646
## [8036]   127.629372   178.741989   292.027191   157.264557   151.534180
## [8041]    78.119423    85.223511   136.313080   135.492310   150.642670
## [8046]   262.199371   178.988266   123.230957   259.202393   256.754761
## [8051]   133.414581   134.607590   301.084625   141.751526   155.095139
## [8056]   119.033951   132.851608    66.519180   127.306610   127.036415
## [8061]   116.025513   236.211548    80.122948   114.048248   132.048035
## [8066]    89.308449   233.077545   141.648392   159.197388   244.705093
## [8071]   188.748993   165.455383   111.856651    99.111298   114.116684
## [8076]   127.379242   114.980789   158.551300   135.596527   124.281128
## [8081]   177.325775   131.374771   118.925453   164.038788   141.445267
## [8086]   150.212524   124.510223   166.669250    90.919052   245.778503
## [8091]   228.775421   177.220535   156.332626   254.089081   168.649490
## [8096]   100.559441   172.320755   120.338852   117.147003   110.950912
## [8101]   269.577240   123.241142   210.243042   142.165619   128.479782
## [8106]   103.158783   214.037949   258.518707   191.623535   169.229675
## [8111]   152.289917   123.595779   112.896805   152.442291   176.253387
## [8116]   101.194046   178.446442   162.285309   147.090714   124.675964
## [8121]   152.783417    54.961632   103.681519   114.140686   102.907463
## [8126]   104.608055   248.292419   115.303284    71.507309   221.065262
## [8131]   226.593063   141.732498    58.713657    88.062408   106.067230
## [8136]   181.073166   320.100952    82.236099   163.909698   236.871017
## [8141]   110.797508   131.507828    83.791901   149.605988   141.706970
## [8146]   345.745911   101.905533   188.199295   114.883255   287.516632
## [8151]   113.096115   167.407684   117.301750    68.716644   115.463348
## [8156]    85.641014    98.504044   148.817123   177.917633    77.626656
## [8161]   132.996246   207.775360   104.965797   202.925842   111.890892
## [8166]    66.155457   141.862503   173.174286   153.421616   314.642273
## [8171]    86.814522   120.464073   121.298340   130.892578    96.762054
## [8176]   275.530701   223.234268   181.874405   176.625168   240.505814
## [8181]   115.289551   122.684296   283.483734    64.922783    73.631416
## [8186]    59.288101   108.197639    78.380745    91.989174   140.962143
## [8191]   232.843307   221.463470   197.528030   118.133072   201.349991
## [8196]   112.352829   112.720375   199.425186   258.724609   223.521851
## [8201]    73.873627   160.374115    88.259232    90.777031   164.436203
## [8206]   161.897217    90.752281   147.083160    82.883423    80.728951
## [8211]    70.136238   129.394699   190.369873    89.477913   140.822495
## [8216]   107.870850    73.727257   292.680023    40.811531   100.113724
## [8221]   197.136108   276.781372   171.586136   115.300537   137.603989
## [8226]   205.544922   109.238792   216.307388   462.340790   127.309967
## [8231]   334.597595    58.474472   123.246521    88.838562   119.693436
## [8236]   143.357605   127.234329   130.552063   129.363998    33.739700
## [8241]   100.274055   113.660919   144.648422   131.237076   214.226532
## [8246]   105.368378   106.523834   169.623154   122.394714   150.369598
## [8251]    78.585663   108.801819   397.853241   101.088730   104.780281
## [8256]   115.422508   105.898003    76.884491   246.480865    92.890923
## [8261]   113.229324   161.778275   276.384583    80.830299   135.271652
## [8266]   169.374268   174.449997   228.385315    91.289291   508.692566
## [8271]   201.279053    73.638062   136.136292   181.846268   201.474609
## [8276]   111.699883   187.411148   196.627304   149.552994   183.268921
## [8281]   179.702820   103.970192   166.202866    66.544174   234.331772
## [8286]    81.127129   107.404129   117.865631   127.897949   266.587769
## [8291]   111.843765   124.385307   116.662811   138.101212   152.073303
## [8296]    58.974834    53.089928   101.433975   138.893539    59.977352
## [8301]   276.243042   147.278778   129.482254   209.778046   250.666519
## [8306]    99.919357    97.025673   171.827942   173.393341   110.692833
## [8311]   269.412292   104.132469   125.116333   133.454285   234.475449
## [8316]   133.114807   134.143967   241.684891   123.002380   159.661087
## [8321]   202.875778   130.824051    93.307816   264.791351   200.376556
## [8326]   216.310715   154.523392   116.917953   159.945679   186.751404
## [8331]   179.701035   258.164124   150.970230   141.809998    92.950882
## [8336]   112.168633   116.431335   176.405228   107.033981   177.461716
## [8341]   118.032776   114.868011   222.788757   279.095032   101.379700
## [8346]   120.830292    63.259624   193.945251    96.399078   107.054245
## [8351]   139.383804   140.499634   137.600220    69.858307   125.205299
## [8356]   326.205322   186.524048   136.759109   147.961777   186.328552
## [8361]   287.302948    80.570724   185.015228   162.240036   133.419693
## [8366]   147.195648   159.667511   158.003769   157.255478   114.870323
## [8371]    54.851028   235.060562   292.881592   127.018539   122.211899
## [8376]    79.607315   324.827423   176.049393   236.005569   128.952545
## [8381]   162.638901    84.089546   311.039948   239.758392   182.024445
## [8386]   171.299240   173.104691   182.605988   285.737640   101.518425
## [8391]   199.414932   183.679062    83.247444   106.639198   150.078430
## [8396]   131.897919   174.527573   227.255524   140.266998    69.244568
## [8401]   360.106995   110.152756   323.202087   196.050079   169.910065
## [8406]    67.097557   108.229317   181.902863   129.140900   197.328751
## [8411]   115.897873   182.130936   228.541031    49.825920   105.220543
## [8416]   161.928680   110.418976   113.560158   114.810989   110.809357
## [8421]   154.459671   120.433807    97.868866    85.645164   155.478561
## [8426]   145.082199    84.502823    90.661873    94.042221   259.238678
## [8431]    60.313995   188.056442   223.257233   107.947495   148.131546
## [8436]   132.446991    58.083206   149.097961   178.482590    74.069809
## [8441]   115.102463    95.493607    52.932003   154.043396   255.501358
## [8446]   141.953476   169.610733    81.970047   199.043320    88.343399
## [8451]   116.112732   228.613953    26.753235   127.120781    46.336418
## [8456]    82.400360   362.030457   240.402985   190.721695   151.761002
## [8461]    86.015587    87.516777   253.576691   156.725845   308.430176
## [8466]   140.405273   235.269836    95.383598    69.561111   125.120476
## [8471]   149.670319   106.777840    71.530907   139.252594   190.236908
## [8476]    40.840023   135.059952   104.310349    66.016800   186.554779
## [8481]   135.159958   119.119949   174.645340   276.134583   100.240822
## [8486]   116.917221   131.306931   149.161163    63.907452   123.813530
## [8491]   104.791145   187.148956   125.605911   187.708862   111.141159
## [8496]    89.892342    85.919395   120.027710   234.417664   158.619400
## [8501]   153.882584   130.999115   137.406036   123.759537   229.855331
## [8506]   137.064392   158.331528   147.451004   163.420425   246.017609
## [8511]   170.229767    87.226837   106.928658   184.732727   111.395111
## [8516]   162.414703    72.439964   130.140793    92.752480   219.694626
## [8521]   162.677780   146.973083   145.655441    97.324806   174.338791
## [8526]   147.871674    85.992020   115.359894   127.598221    93.444725
## [8531]   124.063309   113.019043 -2657.951660   161.527618   157.506454
## [8536]   196.233398   110.099998   134.656113   170.377609   145.233658
## [8541]   152.820007   217.643372   107.468582   279.044128   102.661682
## [8546]   194.151138   201.717346    47.545532    50.365189    81.689720
## [8551]   186.358856   207.418533   216.252945   104.269379   180.869492
## [8556]    97.855080   151.765594    84.491272   115.847931    94.729385
## [8561]   286.918365   101.097656   118.180267   269.513245   103.978439
## [8566]    89.602547   136.322510   176.426620   169.356094   161.488205
## [8571]   160.293396   148.804993   110.556053   125.929947   114.462494
## [8576]    87.870605   129.711960    82.950104   186.367615    92.355446
## [8581]    64.961105   167.870483   107.306488    46.433430   257.903687
## [8586]   134.911667   112.490715    82.714851   109.356415    72.935944
## [8591]    86.305481   110.615654   107.112320   201.879623   159.275681
## [8596]   101.809143   153.337311    87.932480   153.575684    92.816322
## [8601]    77.615814   133.189697   128.886688   113.506119   160.132828
## [8606]    91.811897   150.505417    96.000938   158.354202   140.601013
## [8611]   178.163712    86.638802   334.467529   103.757530   170.470444
## [8616]   115.866821   131.590469   117.862236   111.733559   136.601105
## [8621]   106.067558   107.002823   156.261734   133.908905   105.574661
## [8626]   146.739700    83.500824   139.160980   117.424492    98.574989
## [8631]   174.785309   118.719025   111.479691   120.825348   192.756927
## [8636]   114.975502    92.263123   228.189316   183.934235   107.659180
## [8641]    60.267479    96.192886   109.290398    79.585976    83.064438
## [8646]   104.378357    71.785355    71.064095    99.783875   110.632965
## [8651]   233.134659   108.412697   123.386482    99.374680   110.369164
## [8656]   116.080368   104.322929   124.605957   103.842239   287.661530
## [8661]   155.252151    88.446541   136.274872   112.012665    87.710182
## [8666]   140.411896   153.595413   174.955139   114.234222   204.783310
## [8671]    62.011227   110.234932    75.057060   142.742874   112.088791
## [8676]   123.548264   425.226135   116.031021   191.894028   144.513550
## [8681]   192.309967   137.686600   283.293182   113.123550   109.756424
## [8686]   110.680511    62.804493   141.940750    96.793701   185.295120
## [8691]   124.114952    86.648964    83.511307   102.430168    75.551613
## [8696]    72.322411   115.595879   195.273392   109.714943   136.019257
## [8701]   103.222656    59.749176   101.799713    72.442619   134.590576
## [8706]   288.675446    64.715202   106.719658   210.969894    91.892967
## [8711]   119.933243   187.687134    98.014984   125.852509   123.833229
## [8716]   174.175232   132.498459    90.265450   218.118652   131.761047
## [8721]   312.480103   154.332962   108.817230    78.603935    62.144650
## [8726]    82.679001   117.833969   121.315628   115.586456   142.446365
## [8731]    91.378639   162.872498   115.115143    96.713219    72.388062
## [8736]   162.996124    93.865479   122.135353   154.897202   124.437523
## [8741]   104.896973   101.964600   119.639336   229.811081   123.284966
## [8746]   142.399338   180.782242   190.055725   106.773727   163.689926
## [8751]   125.971016   105.284370    67.168205   155.972656    82.991447
## [8756]   201.278183   155.968872    65.593292   144.143646   127.018440
## [8761]   327.621063    70.530121   141.884766   137.712662   185.377335
## [8766]   110.057732   167.654953   151.941605   113.893280   100.482819
## [8771]   125.043198   159.408691   519.095642   121.131485   103.560875
## [8776]   131.679642   155.511169    93.565201   160.343796   138.287338
## [8781]   155.103363   154.074371   448.165070   272.822205   139.078781
## [8786]   184.321030   105.930016   113.053864    91.860191   165.744400
## [8791]   191.172165    92.912109   142.670349   105.916473   155.235672
## [8796]   103.804344   116.111130    99.312256   183.497681   155.809097
## [8801]   104.401253   108.319580    89.235184   275.940704   129.518585
## [8806]   184.087784   143.198944   196.010376    90.278915   151.468369
## [8811]   108.141045    96.847778   126.356133   145.266953   124.168739
## [8816]   139.891998   350.617218   224.219437   174.985931   204.789383
## [8821]   158.751495   137.957672   187.289841   158.396332   123.854538
## [8826]    81.488579   155.809647   112.122093   177.828323    98.227097
## [8831]   155.138519   133.765045    89.926125   395.433167   116.658157
## [8836]   174.650772   122.537651   137.132614   259.386871   141.529282
## [8841]   104.704727   196.361084   144.958649    95.752914   238.500595
## [8846]   249.078445   108.498604   109.037498   163.198074   130.035721
## [8851]   133.985779   127.622978   107.074707   101.129036   114.044212
## [8856]   141.714127   142.738602   134.709564   158.823578   123.549057
## [8861]   118.643631   120.683945   119.158737   166.712234   134.883408
## [8866]   143.297394   294.777344    92.500893   261.060242   151.653671
## [8871]    65.049911   128.635773   120.811310    92.337448   149.658508
## [8876]   129.946106   129.437454   139.836670   145.697128   217.118317
## [8881]   164.711807   162.033127   139.147644   115.593643   155.514130
## [8886]   119.157959   116.477905   169.119263   144.834198   161.486649
## [8891]   203.229752   169.935287   186.974350   180.041382   237.727966
## [8896]   117.220642   148.778687   167.644287   140.451279   259.594910
## [8901]   147.157089   219.279449    95.639900   191.926788   101.493683
## [8906]   152.835983   118.740273   162.332108   113.259377   310.769775
## [8911]   114.235077    96.286087   153.591248    89.029968   140.179153
## [8916]   101.896347    96.062714    72.690628   111.835457   148.943970
## [8921]   120.417854    92.326103   126.146095   100.961998   257.080536
## [8926]   269.577759   103.016258   114.332016    93.359520    95.126900
## [8931]   196.520889    62.684719   118.938164    72.062050   117.723381
## [8936]    86.553139   159.716858    93.644043    97.793777    94.354790
## [8941]   164.085083   127.429039   120.190620   150.690536   225.606186
## [8946]    82.359291   174.162399   153.835083    99.493156   128.108368
## [8951]   180.322647   155.951691    82.074821   138.943253    89.786705
## [8956]   145.537872   110.660980   126.580307   137.372192    97.902061
## [8961]    98.379501   251.269226   124.217041    87.373787   103.777649
## [8966]   145.762070   122.556122   163.841766   134.815582   135.818237
## [8971]   186.953796   223.759506    55.794098   201.428101   106.808998
## [8976]    70.793404   136.472000   113.450897   101.607765   103.258492
## [8981]   214.924438   173.611771   126.590660    78.357063   248.659409
## [8986]   127.296936   190.063477   180.266769   119.508942    65.662598
## [8991]    76.016716   103.736519    78.403557    91.155365   208.557556
## [8996]   112.132072   102.533279   178.710037    93.037079   162.941055
## [9001]   140.157333    77.224869   201.526794   120.275902   107.024956
## [9006]   137.415543    81.842903   133.232224   110.685974   149.791306
## [9011]   185.432358    94.341667   125.157944   113.771217   107.307617
## [9016]   117.996239   153.197540   140.735352   157.054382   291.630646
## [9021]    94.063957   154.862228   201.594604   202.040497   133.739685
## [9026]   110.897797    81.094688   109.565796   111.136810   168.109299
## [9031]   150.235138   165.492508   192.782089   204.585419   170.396317
## [9036]    96.105103   334.325500    80.942200   145.346115   107.789108
## [9041]    82.969643   128.449326   144.836029    73.511330   146.809433
## [9046]    61.358856   124.891937   170.967422   136.583084   189.712448
## [9051]   129.417358    63.731106   117.456940    59.841587   136.481354
## [9056]    82.149071    68.113213   123.989334   111.629181   124.047966
## [9061]    83.238480   101.398216   188.989655   135.800522   134.914764
## [9066]   138.303146   172.152603   144.752136    90.181923   120.558914
## [9071]   232.473404    62.525124   114.871620   152.940628   153.040970
## [9076]    87.696518    93.663673   100.198997   167.698792   137.359161
## [9081]   173.730652   198.038803    86.856339   105.234413   103.179710
## [9086]    98.063812   104.013252   172.844986    69.472153   202.878448
## [9091]   116.769623   107.121864   134.195404   211.799805   148.547485
## [9096]   116.189056   114.245125   156.398163    73.736916   264.329437
## [9101]   180.021591   110.969475   202.758423   163.842484   103.038338
## [9106]   142.245239   240.099365   120.140411   181.289154   216.367920
## [9111]   140.573624   188.695618   217.860001   109.473083   161.561218
## [9116]   133.688782   103.811981   163.908356    88.657707    91.640396
## [9121]   104.877304   147.022980   117.471130   274.716492    89.146103
## [9126]   129.022949   130.533096   110.522812   153.616440   140.368866
## [9131]   221.406693    63.828037   129.857697   109.915192    62.084724
## [9136]   113.520615   211.589233   160.884262   173.227356   111.727119
## [9141]   113.251190   189.548691   294.404358   233.345352   178.235168
## [9146]   105.896080   147.209518   140.521805   121.323822   117.539665
## [9151]   120.954437    95.032715   148.020844   116.543182   147.340698
## [9156]    91.647278   174.061325   103.768097   123.677437    99.729889
## [9161]   161.364258   188.818695   174.639191   292.948517   107.870926
## [9166]   105.076889   146.472595   118.577858   132.667160   259.757935
## [9171]    75.817207   118.726578   134.873749    93.214310   145.411072
## [9176]    97.533485   184.071762    72.488358   115.381958   156.816071
## [9181]   140.975571    68.286423   123.774475   101.744186   153.129959
## [9186]   140.051102   171.603989   101.906013    83.654793    90.842796
## [9191]    92.954239   127.511322   176.256790    77.419144    64.827591
## [9196]   121.331619   104.028999   137.100281   177.293671    90.682922
## [9201]   192.408722   361.605011   198.273529   111.368553   105.138916
## [9206]   124.289719    75.338852    74.890152   205.167236   132.467560
## [9211]   107.189728   113.088753   123.874542   201.081528   100.228035
## [9216]   179.286667   171.010727   208.675873   153.828934   198.476624
## [9221]   113.416733   141.038193   215.722198   112.849716   102.891533
## [9226]    73.264587   215.034729   203.375809    74.462959   104.637383
## [9231]   164.580811    74.553673    84.912560   317.723999   150.079956
## [9236]   111.952225   117.141403   106.028244    83.943581    99.470894
## [9241]   165.951782   101.749451   226.645111   139.074905   154.717896
## [9246]    74.653160   116.749695   209.494431   143.637939   201.887405
## [9251]    86.751740    78.460793   105.786469   108.133820    96.745094
## [9256]   229.567139   187.187119   141.086990    82.644859   112.876129
## [9261]   134.123260   103.599152   196.791443   121.505760   103.773888
## [9266]   142.884064   206.853241    74.136246   173.616348   179.071594
## [9271]    94.809219   121.098579   292.401855   136.049072    98.300362
## [9276]   174.725357   145.626770   113.290009   127.901184   115.328659
## [9281]   183.125412    95.547897    89.056740   150.908829    94.336128
## [9286]   178.684402   111.280098   143.078033   107.828964   194.941284
## [9291]   110.577713   269.726471   131.887268    94.991127   183.600464
## [9296]   110.148819   124.406158   128.528519   179.003006   163.587204
## [9301]   151.801788   237.799316   163.769455   138.356537   117.224983
## [9306]    83.735146   116.531372    77.008553   195.391418   128.876022
## [9311]   115.956200   105.692886   254.584290   121.168167   162.277008
## [9316]   208.748947    84.871979   260.307648   183.982452   147.919952
## [9321]   174.320251   123.070366   122.590790   100.140625   169.667862
## [9326]   148.533951   114.442352   124.535873    83.128319    53.356033
## [9331]   103.143044    52.461239    70.643585   146.272003    98.236908
## [9336]   148.538193   141.254288   107.449379   122.760643   116.022331
## [9341]    53.653236   159.582016    67.502602   127.020134   159.486481
## [9346]    86.255295   130.614578   224.567276    93.601913   130.224762
## [9351]   143.252197   121.177284   118.616653   112.735191    93.468887
## [9356]   109.453522   113.380737    72.465996    55.218575   207.968002
## [9361]   104.013657    95.283432   157.351364   105.337708   217.687668
## [9366]   147.460373   131.122986   497.960724   104.629639   302.772949
## [9371]   156.086472   109.801849   180.476913   177.952286   146.348831
## [9376]    61.451313   111.192368   140.257812   160.372925   118.252220
## [9381]    91.551117   217.694504   103.197403   104.785683   121.100784
## [9386]   135.946671   148.634308   123.807480   121.055862    83.009979
## [9391]    91.946892   156.768372    94.606644    86.066719    60.053822
## [9396]   192.980469    80.108978   201.269455   114.034195   215.603714
## [9401]   121.041283    88.244789   135.299942   170.703323   219.242966
## [9406]   116.572685    76.588287   173.336487   158.747055   104.901596
## [9411]   121.776283    66.344231   231.455994   125.970230   105.319984
## [9416]    64.371429   109.954277    69.011482   146.661758    84.764954
## [9421]   190.974182   119.871384    85.735611    95.881378   152.186081
## [9426]    66.141838   199.027054   106.570412   107.526779   116.862839
## [9431]    41.930859   105.844353   124.397339   133.258087    81.137962
## [9436]   157.433670   220.759277    86.916252    95.885361   160.148224
## [9441]   114.488052   105.839233   112.110535   104.729065    83.617279
## [9446]    97.312744   205.280975    74.381821    46.978237   101.778786
## [9451]   192.894287   176.018921   167.212799    83.049240   128.888580
## [9456]    90.650169   100.045769   175.925476    94.490906    71.727570
## [9461]    83.120590   126.067879    94.842697   135.814484   153.952637
## [9466]   137.965149   146.614059   178.761047    69.908607   276.263580
## [9471]   135.514954   143.989868   147.050522   121.656105   123.547653
## [9476]   126.048866    75.541656   123.232086   208.445679   105.872879
## [9481]    85.731461   113.532303   103.660736   215.357452   163.804291
## [9486]   115.481934   128.748917   123.640114    81.751175   105.659599
## [9491]   198.439117    80.627090   105.142014    80.350372    76.169281
## [9496]   158.042542   105.889908   152.424393   189.096664   133.118698
## [9501]   262.875519   146.944229    69.360832   118.504578    94.624550
## [9506]   115.966934   148.154541   124.094955   118.321014   138.665634
## [9511]   141.645996   122.750793   131.845627   160.978729   248.892639
## [9516]   186.028290   164.611893   185.811661   111.278740    85.462166
## [9521]   209.953461    72.234283   140.433014    87.220863   152.487747
## [9526]   213.223419   107.974808   101.540802   360.350494    84.147232
## [9531]    96.413055    89.662224   142.671783    73.883224   101.071465
## [9536]   112.626900   206.380951   229.742676   137.163208   175.514435
## [9541]   185.073410   273.253693   151.520859   110.536636   196.656998
## [9546]   125.024017   127.590668    70.070107   111.071350   507.913483
## [9551]   210.053162    98.731567   109.355179   138.423630   123.450943
## [9556]   115.774048   100.952629    91.202538    86.983772    82.994164
## [9561]   194.716492    40.313316   174.253815   169.193192   169.669739
## [9566]   139.822037    99.243225   147.841156   129.072235   134.528305
## [9571]    74.275497   174.161179    86.806061   262.494080    89.050415
## [9576]   111.483116    78.880806   104.085030   195.311920    72.853851
## [9581]   195.332809   154.209991   157.914047   235.277802   189.285751
## [9586]   113.478264    74.473656   241.567062   120.808044   178.912766
## [9591]   114.223572   142.666046   160.088058   133.395676   128.867111
## [9596]   140.332764   125.710022   136.486130    93.581161   136.910141
## [9601]   150.110626   196.388016    85.101624   214.920456    95.857033
## [9606]   208.425491   174.038971   179.382034   122.108604    56.527874
## [9611]   180.709991   153.121399    63.319183   108.992439    63.927273
## [9616]   194.610565   113.663528    58.663151   110.267365    91.568291
## [9621]    59.692974   118.060364   130.503601    51.388844   206.112549
## [9626]   112.049202   258.367828   116.216888   151.665527   110.140457
## [9631]    84.731331    98.334312   175.542755   119.880775   164.772690
## [9636]    79.580544   103.833260   127.398705   114.095428   181.671600
## [9641]   258.177734   214.251831   163.816757   236.802597    64.746887
## [9646]   296.279053   104.659843    71.878700   168.671829    97.533272
## [9651]   109.521896   521.002136   270.047882   154.576981   215.400589
## [9656]   187.070450   135.638367    69.945091   189.035202    72.484169
## [9661]    78.436646    87.069397   117.145058   103.361565   100.994469
## [9666]    72.742226    84.559784   124.125648   120.270660   193.847443
## [9671]   130.575638    96.996132   119.907806   134.126129   114.174530
## [9676]   125.003799   106.064957   136.608658   101.025475   132.338745
## [9681]    58.788540   131.223053   158.705994   103.306152   117.343552
## [9686]    98.215805   152.087219   170.120941   242.927292    83.919662
## [9691]   139.853241   259.618195   371.754791    46.509224   197.064804
## [9696]   183.871063   136.756119   128.187973   121.618118   169.671600
## [9701]   346.342682    74.485420   305.228027   199.972305    84.841621
## [9706]   119.337700    98.051193   242.267395   218.500687   153.088394
## [9711]   171.818283   136.350952   194.872330   148.786407   195.900925
## [9716]   214.236053   145.249084   174.695831   165.775391   218.522568
## [9721]   188.432373   144.968201   229.734497   103.483971   212.992523
## [9726]   313.530365    82.062355   138.284744    88.600235    87.440575
## [9731]    55.585869   111.006325   241.239090   114.412720   120.960999
## [9736]    99.915642    97.972115   128.625580   110.134163    86.202812
## [9741]   212.262421    98.698654    65.653748    38.161533    84.678574
## [9746]   -29.405849   178.022827    64.222694    50.388779   110.607391
## [9751]   140.066330   114.830956    81.241882   161.039642   104.211235
## [9756]   102.627495   150.556641   248.806152   -30.547649   298.229309
## [9761]   159.680771   197.523209   -27.754459    66.798225    85.254799
## [9766]   133.779617   165.278625   207.641891   -59.277031   159.529175
## [9771]   -43.773167   145.670273   102.049911    82.593529   174.503189
## [9776]   280.172241   138.242493   131.005493   173.387054

Wordcloud

Description= data1$summary

data2<-Corpus(VectorSource(Description))
data2<-tm_map(data2,stripWhitespace)
## Warning in tm_map.SimpleCorpus(data2, stripWhitespace): transformation
## drops documents
data2<-tm_map(data2,tolower)
## Warning in tm_map.SimpleCorpus(data2, tolower): transformation drops
## documents
data2<-tm_map(data2,removeNumbers)
## Warning in tm_map.SimpleCorpus(data2, removeNumbers): transformation drops
## documents
data2<-tm_map(data2,removePunctuation)
## Warning in tm_map.SimpleCorpus(data2, removePunctuation): transformation
## drops documents
data2<-tm_map(data2,removeWords, stopwords('english'))
## Warning in tm_map.SimpleCorpus(data2, removeWords, stopwords("english")):
## transformation drops documents
data2<-tm_map(data2,removeWords, c('and','the','our','that','for','are','also','more','has','must','have','should','this','with'))
## Warning in tm_map.SimpleCorpus(data2, removeWords, c("and", "the", "our", :
## transformation drops documents
wordcloud(data2, scale=c(3,0.5), max.words=1000, 
          random.order=FALSE, rot.per=0.35, use.r.layout=FALSE, colors=brewer.pal(8,'Dark2'))
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : roommates could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : university could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : spaces could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : restaurant could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : museums could not be fit on page. It will not be
## plotted.
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## random.order = FALSE, : supermarkets could not be fit on page. It will not
## be plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : main could not be fit on page. It will not be
## plotted.
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## random.order = FALSE, : know could not be fit on page. It will not be
## plotted.
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## random.order = FALSE, : supermarket could not be fit on page. It will not
## be plotted.
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## random.order = FALSE, : slope could not be fit on page. It will not be
## plotted.
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## random.order = FALSE, : microwave could not be fit on page. It will not be
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## random.order = FALSE, : sunlight could not be fit on page. It will not be
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## random.order = FALSE, : stations could not be fit on page. It will not be
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## random.order = FALSE, : peaceful could not be fit on page. It will not be
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## random.order = FALSE, : music could not be fit on page. It will not be
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## random.order = FALSE, : centrally could not be fit on page. It will not be
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## random.order = FALSE, : outside could not be fit on page. It will not be
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## random.order = FALSE, : walkup could not be fit on page. It will not be
## plotted.
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## random.order = FALSE, : washington could not be fit on page. It will not be
## plotted.
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## random.order = FALSE, : comes could not be fit on page. It will not be
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## random.order = FALSE, : greenpoint could not be fit on page. It will not be
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## random.order = FALSE, : provide could not be fit on page. It will not be
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## random.order = FALSE, : bedstuy could not be fit on page. It will not be
## plotted.
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## random.order = FALSE, : we’ve could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : skyline could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : visit could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : warm could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : metro could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : patio could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : level could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : prewar could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : beautifully could not be fit on page. It will not
## be plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : recently could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : throughout could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : middle could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : town could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : cleaning could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : ferry could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : decor could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : crown could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : theres could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : greene could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : essentials could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : takes could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : ceiling could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : linens could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : staying could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : tree could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : apple could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : water could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : book could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : may could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : comfort could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : places could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : ground could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : stylish could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : facing could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : smart could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : convenience could not be fit on page. It will not
## be plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : galleries could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : madison could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : screen could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : travel could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : stunning could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : service could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : lounge could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : options could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : breakfast could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : summer could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : barclays could not be fit on page. It will not be
## plotted.
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## random.order = FALSE, : speed could not be fit on page. It will not be
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## random.order = FALSE, : excellent could not be fit on page. It will not be
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## random.order = FALSE, : exploring could not be fit on page. It will not be
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## random.order = FALSE, : phone could not be fit on page. It will not be
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## random.order = FALSE, : might could not be fit on page. It will not be
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## random.order = FALSE, : hamilton could not be fit on page. It will not be
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## random.order = FALSE, : fabulous could not be fit on page. It will not be
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## random.order = FALSE, : sleeper could not be fit on page. It will not be
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## random.order = FALSE, : boasts could not be fit on page. It will not be
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## random.order = FALSE, : charge could not be fit on page. It will not be
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## random.order = FALSE, : weekend could not be fit on page. It will not be
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## random.order = FALSE, : provides could not be fit on page. It will not be
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## random.order = FALSE, : desirable could not be fit on page. It will not be
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## random.order = FALSE, : staten could not be fit on page. It will not be
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## random.order = FALSE, : complimentary could not be fit on page. It will not
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## random.order = FALSE, : choose could not be fit on page. It will not be
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## random.order = FALSE, : bedding could not be fit on page. It will not be
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## random.order = FALSE, : iconic could not be fit on page. It will not be
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## random.order = FALSE, : graham could not be fit on page. It will not be
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## random.order = FALSE, : onsite could not be fit on page. It will not be
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## random.order = FALSE, : hosts could not be fit on page. It will not be
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## random.order = FALSE, : able could not be fit on page. It will not be
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## random.order = FALSE, : step could not be fit on page. It will not be
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## random.order = FALSE, : seating could not be fit on page. It will not be
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## random.order = FALSE, : blueground could not be fit on page. It will not be
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## random.order = FALSE, : entry could not be fit on page. It will not be
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## random.order = FALSE, : almost could not be fit on page. It will not be
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## random.order = FALSE, : dates could not be fit on page. It will not be
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## random.order = FALSE, : artistic could not be fit on page. It will not be
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## random.order = FALSE, : taxes could not be fit on page. It will not be
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## random.order = FALSE, : chinese could not be fit on page. It will not be
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## random.order = FALSE, : utensils could not be fit on page. It will not be
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## random.order = FALSE, : projector could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : apollo could not be fit on page. It will not be
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## random.order = FALSE, : reservation could not be fit on page. It will not
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : coliving could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : total could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : numerous could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : pullout could not be fit on page. It will not be
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## random.order = FALSE, : flight could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : bam could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : citys could not be fit on page. It will not be
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## random.order = FALSE, : lights could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : organic could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : july could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : interior could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : taking could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : toaster could not be fit on page. It will not be
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## random.order = FALSE, : port could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : fifth could not be fit on page. It will not be
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## random.order = FALSE, : meet could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : skylight could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : bryant could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : eatin could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : clothes could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : hub could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : iron could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : nearest could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : avenues could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : twobedroom could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : granite could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : urban could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : fine could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : daily could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : early could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : pharmacies could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : liberty could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : kitchens could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : deposit could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : buildings could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : fan could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : uptown could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : myrtle could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : carroll could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : flatbush could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : whether could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : call could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : comforts could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : six could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : piano could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : section could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : stuy could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : wont could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : visitors could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : citi could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : budget could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : party could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : italian could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : sublet could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : play could not be fit on page. It will not be
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## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : energy could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : oversized could not be fit on page. It will not be
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## random.order = FALSE, : hair could not be fit on page. It will not be
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## random.order = FALSE, : watch could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : luggage could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : kept could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : authority could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : amazon could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : thats could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : guggenheim could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : accommodates could not be fit on page. It will not
## be plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : channels could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : since could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : serene could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : sunlit could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : simply could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : last could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : photos could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : sights could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : jazz could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : ensuite could not be fit on page. It will not be
## plotted.
## Warning in wordcloud(data2, scale = c(3, 0.5), max.words = 1000,
## random.order = FALSE, : mirror could not be fit on page. It will not be
## plotted.

Conclusion: It can be concluded from the above analysis that When the four neighbourhoods are compared, we can conclude that Manhattan and Brooklyn have higher number of properties listed and have higher number of zipcodes when compared to Queens and Staten Island.

We also observe a huge difference in price per night for properties in Manhattan and have a few outliers. Queens and Staten Island in contrast have a consistent price per night for the properties listed.

Zipcodes in Manhattan have properties with the highest price per night asking.

The mean property cost of properties in Manhattan is very high, and so is the price per night of stay. Hence this neighbourhood has the highest breakeven period.

We observe that Staten Island zipcodes have lower mean cost of properties, but fairly high price per night making it the most profitable neighbourhoods.

We can also conclude from our analysis that Brooklyn zipcodes are doing a pretty decent job in terms of breakeven period. Properties have average cost in this zipcodes and the price per night on Airbnb also are neither too high nor too low

Our sentimental analysis tells us whether the summary provided by the owner for a room/apartment appeals the customers in a positive or a negative way. The word cloud shows us the intensity of the words that tells us about the word frequency. So, in this case we have made word clouds for the positive as well as the negative summary. This would help the owners to change their way towards the marketing side, so that they can use the most used positive words in order to increase their customer staying at their place resulting in more revenue for themselves